Available via license: CC BY 4.0
Content may be subject to copyright.
Citation: Shafii, N.Z.; Saudi, A.S.M.;
Pang, J.C.; Abu, I.F.; Sapawe, N.;
Kamarudin, M.K.A.; Mohamad,
M.H.N. Association of Flood Risk
Patterns with Waterborne Bacterial
Diseases in Malaysia. Water 2023,15,
2121. https://doi.org/10.3390/
w15112121
Academic Editor: Roger Falconer
Received: 9 March 2023
Revised: 5 May 2023
Accepted: 24 May 2023
Published: 2 June 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
water
Article
Association of Flood Risk Patterns with Waterborne Bacterial
Diseases in Malaysia
Nur Zahidah Shafii 1, Ahmad Shakir Mohd Saudi 1, * , Jyh Chyang Pang 2, Izuddin Fahmy Abu 3,
Norzahir Sapawe 4, Mohd Khairul Amri Kamarudin 5and Mohamad Haiqal Nizar Mohamad 4
1Center for Water Engineering Technology, Malaysia France Institute (MFI), Universiti Kuala Lumpur (UniKL),
Bandar Baru Bangi 43650, Selangor, Malaysia; nurzahidahshafii@gmail.com
2Clinical Laboratory Science Section, Institute of Medical Science Technology (MESTECH),
Universiti Kuala Lumpur (UniKL), Kajang 43000, Selangor, Malaysia; jcpang@unikl.edu.my
3Institute of Medical Science Technology (MESTECH), Universiti Kuala Lumpur (UniKL),
Kajang 43000, Selangor, Malaysia; izuddin@unikl.edu.my
4Malaysian Institute of Chemical and Bioengineering Technology (MICET), Universiti Kuala Lumpur (UniKL),
Alor Gajah 78000, Malacca, Malaysia; norzahir@unikl.edu.my (N.S.); haiqalnizar@gmail.com (M.H.N.M.)
5Faculty of Applied Social Science, Universiti Sultan Zainal Abidin (UniSZA), Gong Badak Campus,
Kuala Nerus 21300, Terengganu, Malaysia; mkhairulamri@unisza.edu.my
*Correspondence: ahmadshakir@unikl.edu.my; Tel.: +60-19-241-3454
Abstract:
Flood risk has increased distressingly, and the incidence of waterborne diseases, such as
diarrhoeal diseases from bacteria, has been reported to be high in flood-prone areas. This study aimed
to evaluate the flood risk patterns and the plausible application of flow cytometry (FCM) as a method
of assessment to understand the relationship between flooding and waterborne diseases in Malaysia.
Thirty years of secondary hydrological data were analysed using chemometrics to determine the
flood risk patterns. Water samples collected at Kuantan River were analysed using FCM for bacterial
detection and live/dead discrimination. The water level variable had the strongest factor loading
(0.98) and was selected for the Flood Risk Index (FRI) model, which revealed that 29.23% of the
plotted data were high-risk, and 70.77% were moderate-risk. The viability pattern of live bacterial
cells was more prominent during the monsoon season compared to the non-monsoon season. The
live bacterial population concentration was significantly higher in the midstream (p< 0.05) during the
monsoon season (p< 0.01). The flood risk patterns were successfully established based on the water
level control limit. The viability of waterborne bacteria associated with the monsoon season was
precisely determined using FCM. Effective flood risk management is mandatory to prevent outbreaks
of waterborne diseases.
Keywords: bacteria population; chemometrics; flood risk; flow cytometry; waterborne diseases
1. Introduction
Floods are the most common type of natural disasters, and can have devastating
impacts on over two billion people around the world [
1
]. According to the International
Disaster Database (EM-DAT), floods affected more people globally and caused more dam-
age than any other type of natural disaster in the 21st century [
2
]. Moreover, 95% of people
living in Asia would be affected, on large and varied landmasses including multiple river
basins, floodplains, and other high-risk zones of natural hazards, as well as the high-density
populations in disaster-prone areas [
3
]. It was also reported that there have been tremen-
dous economic losses worldwide for low-, middle-, and high-income countries due to
flood damages [
2
,
4
–
6
]. Flood magnitude and frequency in various locations are expected
to increase over time as a result of uncontrolled development and climate change [1,7,8].
Flood risk in Malaysia has increased distressingly in recent decades. Malaysia is one
of the Asian countries predisposed to flood risk, with approximately 33,298 km
2
of the
Water 2023,15, 2121. https://doi.org/10.3390/w15112121 https://www.mdpi.com/journal/water
Water 2023,15, 2121 2 of 26
land area of Malaysia vulnerable to flood disasters and at least 4.7 million people affected
by flooding annually [
9
]. It was estimated that 85 out of 189 river basins were prone to
recurring flooding [
10
,
11
]. The main consequences of the occurrence of flooding in Malaysia
are due to the nature of physical topography and drainage, as well as the combination of
meteorological, hydrological, and socio-economic factors [
10
,
12
,
13
]. The flood losses and
damages can be extremely high, but historically disastrous flood events caused by rapid
development and environmental degradation are often quickly forgotten [14].
An unpredictable massive flood disaster is generally associated with an increased
risk of waterborne infectious diseases due to the direct impact of floodwater. Outbreaks
of waterborne infections, such as cholera, typhoid, paratyphoid, leptospirosis, E. coli,
and hepatitis A, have reportedly increased in highly vulnerable populations in flooded
areas [
15
–
17
]. Nevertheless, the risk was low unless significant factors such as population
displacement, unsafe drinking water, food scarcity, lack of accessibility to basic healthcare
services, and low immunity level to vaccine-preventable diseases were present [
5
,
17
,
18
].
Furthermore, waterborne disease outbreaks have been linked to floods, and caused by the
consumption of contaminated drinking water after heavy rain [5,15,17,19].
Detection of waterborne pathogens plays a crucial role in monitoring water safety and
sanitation, hence reducing threats that lead to contamination of water sources [
20
]. Many
studies have been conducted to better understand the microbial waterborne pathogens and
diseases associated with flooding, but there is currently no unified approach to collecting
and analysing a water sample for all pathogenic microorganisms [
21
]. Although a number
of different methods, including conventional culture-based and molecular methods, have
been used for enumeration and detection of bacteria from environmental sources, several
limitations and disadvantages remain, thereby creating a challenge in the environmental
microbiology field, and particularly related to floods.
As floods are associated with an increased potential health risk of infection, the
methodological issues associated with the enumeration and detection of microbial viability
in floodwater, as well as the understanding of the synergistic linkage between floods and
waterborne diseases, remain a major gap. Therefore, this study aims to address this issue
by evaluating the flood risk patterns and the plausible application of flow cytometry (FCM)
as an assessment method to understand the linkage between flood events and waterborne
bacterial diseases. This study is the first environmental study related to flooding that was
conducted in Malaysia to improve the surveillance of waterborne infectious diseases, thus
minimising the impacts of flood events on public health issues.
2. Materials and Methods
2.1. Study Design
An integrated approach was employed in this study that consisted of a statistical
analysis for the Flood Risk Index (FRI) model and a cross-sectional study of the linkage
between floods and waterborne bacterial diseases. The statistical analysis utilised chemo-
metrics techniques to analyse a set of databases for hydrology and flooding. Meanwhile,
the cross-sectional study was conducted to determine the viability of waterborne pathogens
in surface water samples taken from flood-prone areas.
2.2. Study Area
Kuantan is the capital state of Pahang, which is also the largest city on the east coast
of Peninsular Malaysia. Kuantan River is the biggest river in the capital city of Pahang
and the primary source of water supply for Kuantan residents. The main river basin of the
river is known as the Kuantan River Basin (KRB), one of the most important river basins in
Malaysia. The KRB is located at the coordinates between the latitude of 3
◦
37
0
50
00
N and
4
◦
07
0
48
00
N and between the longitude of 102
◦
48
0
00
00
E and 103
◦
23
0
40
00
E. The river has
a watershed boundary of 1638 km
2
and a total length of approximately 86 km. Kuantan
receives 2470 mm of annual rainfall, with the highest reading typically occurring during
the Northeast Monsoon season, which is from November to February [
14
]. The district is
Water 2023,15, 2121 3 of 26
exposed to flooding risk approximately three or four times per year, affecting a total of
607,778 Kuantan district residents [
22
]. The study area was chosen because of the district’s
frequent flooding.
The hydrological stations of the Department of Irrigation and Drainage Malaysia (DID)
and water sampling sites are shown in Figure 1. The DID hydrological stations consist of
rainfall station, suspended sediment station, stream flow station, and water level station for
the Kuantan River. Furthermore, water samples were collected at three sampling locations
along the river based on the geographical location of water sources, namely, the upstream,
midstream, and downstream.
Water 2023, 15, x FOR PEER REVIEW 3 of 27
watershed boundary of 1638 km2 and a total length of approximately 86 km. Kuantan
receives 2470 mm of annual rainfall, with the highest reading typically occurring during
the Northeast Monsoon season, which is from November to February [14]. The district is
exposed to flooding risk approximately three or four times per year, affecting a total of
607,778 Kuantan district residents [22]. The study area was chosen because of the district’s
frequent flooding.
The hydrological stations of the Department of Irrigation and Drainage Malaysia
(DID) and water sampling sites are shown in Figure 1. The DID hydrological stations con-
sist of rainfall station, suspended sediment station, stream flow station, and water level
station for the Kuantan River. Furthermore, water samples were collected at three sam-
pling locations along the river based on the geographical location of water sources,
namely, the upstream, midstream, and downstream.
Figure 1. The Department of Irrigation and Drainage Malaysia (DID) hydrological stations and
water sampling locations.
2.3. Flood Risk Patterns Using Chemometrics Techniques
2.3.1. Data Collection of Hydrological Data
Secondary hydrological data from selected DID hydrological stations in Kuantan
were compiled over a thirty-year period (1987–2017). The data were retrieved from the
National Hydrological Network Management System (SPRHiN) website
(http://sprhin.water.gov.my/, accessed on 5 May 2023), accessed on 8 January 2019. De-
tailed information regarding data, particularly the type of variable, the name of the sta-
tions, the coordinates of the stations, and the dates and years for all data were collected
by the competent DID officers, either manually or via telemetry, and stored in the DID
databank system. The data were analysed using chemometrics techniques using XLSTAT
software (Addinsoft, New York, NY, USA) and JPM software (SAS Institute, Cary, NC,
USA).
2.3.2. Data Analysis Using Chemometrics
• Factor Analysis (FA)
FA measures the underlying, error-free latent (unobserved) variables and allows the
inclusion of a large number of many correlated variables in a smaller set of variables
known as factors. This technique manages the data by recognising the most useful and
significant variables as a result of variations in spatial and temporal characteristics that
can describe the entire dataset for analysis. It also functions by minimising the loss of
original information and the statement is supported by the FA equation, where the FA is:
Figure 1.
The Department of Irrigation and Drainage Malaysia (DID) hydrological stations and water
sampling locations.
2.3. Flood Risk Patterns Using Chemometrics Techniques
2.3.1. Data Collection of Hydrological Data
Secondary hydrological data from selected DID hydrological stations in Kuantan were
compiled over a thirty-year period (1987–2017). The data were retrieved from the National
Hydrological Network Management System (SPRHiN) website (http://sprhin.water.gov.
my/, accessed on 5 May 2023), accessed on 8 January 2019. Detailed information regarding
data, particularly the type of variable, the name of the stations, the coordinates of the
stations, and the dates and years for all data were collected by the competent DID officers,
either manually or via telemetry, and stored in the DID databank system. The data were
analysed using chemometrics techniques using XLSTAT software (Addinsoft, New York,
NY, USA) and JPM software (SAS Institute, Cary, NC, USA).
2.3.2. Data Analysis Using Chemometrics
•Factor Analysis (FA)
FA measures the underlying, error-free latent (unobserved) variables and allows the
inclusion of a large number of many correlated variables in a smaller set of variables
known as factors. This technique manages the data by recognising the most useful and
significant variables as a result of variations in spatial and temporal characteristics that can
describe the entire dataset for analysis. It also functions by minimising the loss of original
information and the statement is supported by the FA equation, where the FA is:
Xi=ai1F1+ai2 F2+ai3F3+· · · +aimFm+ei(1)
Water 2023,15, 2121 4 of 26
where X is the measured variable, F is the factor score, a is the factor loading, i is the sample
number, m is the total number of factors, and e is the measurement error or variance.
The varimax rotation process was applied to maximise the difference between the
variables, facilitating an easy interpretation of the data [
23
]. The process was also used
to produce new groups of variables known as varifactors (VFs). The number of variables
that had similar features, and unobservable and hypothetical data, would be the same as
the number of VFs obtained from the varimax rotation process. After obtaining the VF,
the level of significance of the variables for factor loadings was classified based on the
ratio from the analysis. Factor loading with a correlation coefficient of more than 0.70 was
regarded as a strong factor loading for further analysis [24].
•Statistical Process Control (SPC)
Time series analysis was very important in predicting water levels in the study area.
Time series analysis was performed using the SPC technique, an analytical control chart
that constantly visualises the level of quality of the selected variables as time passes. The
control chart establishes control limit lines that are used as a measurement for the quality
condition by adhering to the specific control limit lines. It can also reveal some trends and
patterns showing actual data deviations from the historical baseline and dynamic threshold,
as well as detecting unusual resource usage that could be the best baseline [24].
For this analysis, the SPC analysed the selected hydrological variable using the factor
loading generated by FA. The control limit values for the selected hydrological variable
were the Upper Control Limit (UCL), Central Control Limit (CCL), and Lower Control
Limit (LCL). The UCL value indicates the maximum capacity that the river can support;
if this value exceeds the limit line, the possibility of flooding is considered very high [
24
].
The equation for this analysis was:
Moving Range =Plot : MRtfor t =2, 3, . . . , m. (2)
where UCL is 3.267MR, CCL is MR, LCL is 0, MR is the average moving range, t is time,
and m represents individual values, which are also associated with:
Average Value : ∼
x=∑m
i=1xi
m(3)
where
∼
x
is the moving range, m represents the individual values, and x
i
is the difference
between data points.
•Flood Risk Index (FRI)
The FRI of the flood risk model was developed on the basis of a combination of
several types of multivariate analysis such as FA, SPC, and ANN techniques. The model
was designed to develop an effective guideline for assessing flood risk in the study area.
This is significant and represents a new breakthrough in the study of flood risk, and the
model demonstrated the ability to be sustained in flood research studies. The process of
creating the FRI was followed by a few statistical analysis processes. First, by applying
FA, the best variable with the highest factor loadings was selected to be applied in the
initial step of the development of the FRI model. Following the selection of the variable,
the determination of the control limit value was progressed through the implementation of
SPC. Using this method, the formation of UCL, CCL, and LCL was able to provide guidance
for determining the FRI ratio. The value of UCL was deemed to be an intolerable value for
a variable and was considered a high-risk flood situation. The UCL value was applied for
the formation of the FRI and the risk index was determined using the equation below:
UCLV
x×100 =70(Value of High −risk Index)(4)
Water 2023,15, 2121 5 of 26
where UCLV is the UCL value of the variable,
x
is the highest value of the data, 100 repre-
sents the range of the risk index, which was from 1 to 100, and 70 is the significant value of
the index for high-risk.
The FRI formula based on the above equation was designed to achieve the best flood
risk model in monitoring the risk of flooding in the study area. The computed values of
the FRI ranged from 0 to 100, and the values were classified into three main categories,
which corresponded to low-risk for 0 to 34, moderate-risk for 35 to 69, and high-risk for
70 to 100. The selection of the range from 70 to 100 for high-risk was adapted from the
Relative Strength Index (RSI) concept, in which 70 was considered to be an upper limit of
overflow, above which values are intolerable. This concept had been applied in previous
studies related to floods [14,24,25].
•Artificial Neural Network (ANN)
An ANN is an Artificial Intelligence (AI) information-processing system designed to
imitate the human brain in data analysis, primarily for the purpose of discovering knowl-
edge, patterns, or models from large amounts of data. The back-propagation algorithm
technique is used in ANNs, which require the training of a multi-layer feed-forward net-
work algorithm composed of an input layer, one or more hidden layers, and an output
layer [
24
]. In this study, the findings of the previous analysis were utilised in the application
of an ANN for the FRI prediction model by determining the prediction accuracy of the
selected variable. The technique also aimed to predict the accuracy of the new FRI that
would be applied to determine the flood risk level by predicting the risk generated by
the actual risk rate. The risk comparisons were intended to provide a more detailed and
significant view of the flood risk level in the affected area.
Two criteria that need to be taken into account to ensure the prediction for each
network is accurate and efficient are the correlation of determination (R
2
) and the root
mean square error (RMSE). A prediction is considered to be more accurate if the R
2
value is
higher and closer to one, and the RMSE value is lower and closer to zero. The equations of
R2efficiency and RMSE can be defined as:
R2=1−∑(x−y)2
∑iy2∑iy2
n
(5)
RMSE =r1
n∑n
i=1(xi−yi)2(6)
where x
i
represents the observed data, y
i
represents the predicted data, and
n
is the number
of observations.
2.4. Viability of Waterborne Bacteria Using FCM
2.4.1. Water Sampling
The water sampling was conducted in two phases of the timeline based on the North-
east Monsoon for seasonal variation. The first phase was carried out during the monsoon
season (November–February), on 18 December 2018, and the second phase took place after
or before the monsoon season, on 14 March 2019. New 1000 mL Thermo Scientific
™
Nal-
gene™ Wide-Mouth High-density Polyethylene (HDPE) bottles were used for the surface
water sampling at each site of sampling. Before being transported to the sampling sites,
all sampling bottles were initially washed with a 70% concentrate ethanol solution (R&M
Chemicals) and allowed to dry [
26
,
27
]. The sampling bottles were stored in a sterilised
portable CoolFreezer CDF-45 (Waeco Mobile Solutions, Dometic Group) that was reserved
exclusively for water sampling purposes. For documentation and reference purposes,
the sampling bottles were labelled with the required details such as the name, location,
coordinates, date, and time of the sample taken at the sampling sites [28,29].
All water samples were collected via the grab sampling technique by filling a container
held beneath the surface of the water to obtain a sample at a particular selected location and
Water 2023,15, 2121 6 of 26
time, which reflected the water composition source at that location and time [
28
–
30
]. To
prevent contamination, the mouth and inside of the sampling cap and bottle were carefully
handled to avoid contact with any non-sterile objects. A small amount of air space was
left in the sample bottles to facilitate mixing before FCM analysis. For preservation, the
collected water samples were stored in a portable freezer box with ice packs at 4
◦
C. As
this study intended to conduct bacterial analysis, the samples were analysed within 24 h
after the collection of water samples. All samples were immediately transported to the
University of Putra Malaysia (UPM) laboratory for FCM analysis.
2.4.2. Bacterial Detection and Live/Dead Discrimination
The bacterial detection and live/dead discrimination by FCM were carried out ac-
cording to BD Biosciences Immunocytometry Systems [
31
]. A BD
™
Cell Viability Kit
with BD Liquid Counting Beads (Catalog No. 349480; Becton, Dickson and Company, BD
Biosciences, San Jose, CA, USA) was utilised for the staining procedure. The BD
™
Cell
Viability Kit contains two fluorescent dyes: propidium iodide (PI) (Becton, Dickson and
Company, BD Biosciences) and thiazole orange (TO) (Becton, Dickson and Company, BD
Biosciences). These two dyes have different characteristics of cell permeability that can
be used to distinguish cells with different integrities of the membrane [
32
]. PI solution
was used for staining dead cells and TO solution was used for staining all cells. Living
cells have intact membranes and are impermeable to dyes such as PI, which penetrates
cells with damaged membranes, while TO is a permeable dye that enters all cells, both live
and dead, to varying degrees. BD Liquid Counting Beads (Becton, Dickson and Company,
BD Biosciences), a flow cytometry bead standard, were applied to enumerate the absolute
count of live, dead, and total bacteria.
The staining procedure started by adding 500
µ
L of water samples collected from
the Kuantan River into a labelled disposable 12
×
75-mm BD Falcon
™
polystyrene test
tube (Becton, Dickinson and Company). The water sample was initially vortexed using
a vortex mixer. Five microliters of each dye solution were added to the tubes with final
concentrations of 420 nM for TO and 48
µ
M for PI. The mixture was briefly vortexed and
incubated for 5 min at room temperature. Fifty microliters of BD Liquid Counting Beads
were added to the staining tube using the reverse pipetting technique to determine the
concentration of live, dead, and total bacteria. The staining tube was capped and gently
vortexed to mix the solution. The final mixture was analysed using a BD LSRFortessa
™
(BD Biosciences, San Jose, CA, USA) analyser equipped with lasers having excitations of
488 nm Blue and 640 nm Red. After the analysis was completed, all stained samples and
extra dye solutions were disposed of in accordance with local standard regulations.
2.4.3. Data Acquisition and Analysis
The FCM data on the water samples were analysed using BD FACSDiva
™
(BD Bio-
sciences, San Jose, CA, USA) software. The data sets were reserved to handle no more
than 10,000 events per second. The unstained sample was also analysed in parallel with
each stained sample to confirm that the voltages of the photomultiplier tubes (PMTs) were
appropriately set up. The bacterial population should be positioned entirely on the scale
on a forward scatter (FSC) plot versus a side scatter (SSC) plot by pre-setting a gating
strategy to discriminate live and dead cells from background noise. The best discrimination
of stained cells was visualised on FL1 (TO fluoresces) versus FL3 (PI fluoresces). Three
main cell populations were expected to be defined, namely, non-damaged viable (live) cells,
intermediate (injured) cells, and membrane-damaged (dead) cells. The concentration of the
bacterial cell populations was determined using the equation shown in Equation (7):
# events in cell region
# events in bead region ×# beads/test
test volume ×dilution factor =concentration of cell population (7)
The data gathered from the FCM analysis were further analysed for the statistical sig-
nificance of bacterial concentrations. Statistical comparison among water sampling station
Water 2023,15, 2121 7 of 26
groups was performed using one-way analysis of variance (ANOVA), while comparison
among season groups was assessed using the unpaired t-test. All analyses were performed
using the GraphPad Prism 9 software (GraphPad Software, San Diego, CA, USA). pvalues
of less than 0.05 (p< 0.05) were considered statistically significant.
3. Results
3.1. Chemometrics Techniques for Flood Risk Patterns
3.1.1. General Descriptive
According to the general descriptive statistical analysis of the hydrological data shown
in Table 1, the mean water level in the Kuantan River was 17.10 m, with a standard deviation
of 0.65 m. The minimum water level was 15.75 m and the maximum water level was 24.69 m.
The stream flow variable was analysed to provide a clear picture of the stream flow rate
in the river. Based on the results, the mean for the stream flow rate was 51.86 m
3
/s, with
the minimum and maximum values recorded at 2.80 m
3
/s and 2164.00 m
3
/s, respectively.
For the suspended sediment variable, the mean value of the suspended sediment inflow
was 846.42 tons/day. According to the hydrological data for the past 30 years, the lowest
value for the suspended sediment in the study area was 1.40 tons/day and the maximum
value was 3985.70 tons/day. Meanwhile, the mean rainfall value for the Kuantan district
was 0.29 mm. No rainfall was recorded at the minimum value as the result was shown at
0 mm. The maximum and heaviest rainfall in the study area was recorded at 83.30 mm.
Table 1. Descriptive statistics of hydrological data in the Kuantan River (1987–2017).
Descriptive Category
Hydrological Variable
Water Level
(m)
Stream Flow
(m3/s)
Suspended Sediment
(Tons/Day)
Rainfall
(mm)
Minimum 15.75 2.80 1.40 0.00
Maximum 24.69 2164.00 3985.70 83.30
Mean 17.10 51.86 846.42 0.29
Median 16.98 29.60 144.40 0.00
Standard Deviation 0.65 94.16 1143.19 1.98
Coefficient of Variation 0.04 1.82 1.35 6.83
The descriptive analysis above describes the characteristics of the data for each of
the hydrological variables. Based on the overall standard deviations analysis, there was
high variability of data for variables of suspended sediment and stream flow, moderate
variability in rainfall, and the least variability in water level. The data of the water level
variable had a very low dispersion as the coefficient of variation (CV) was less than one,
whereas other variables had a CV of more than one. Therefore, the analysis revealed that
the water level variable had good data homogeneity, with variability values of 4% [33].
3.1.2. Identification of the Significant Factors
The data in this study were further analysed with FA to determine the most significant
variables that contributed to the flood events. The FA was applied to hydrological variables
and the most significant factor loading reflected the most significant variable with the
strongest association with the underlying latent variable. The most significant variable
from this analysis was selected and applied to design the FRI model for the flood warning
system, which could serve as a useful tool in flood preparation and management.
Figure 2shows the diagram of the scree plot used to evaluate the cut-off point of the
strong factors selected for interpretation. The diagram shows that only one of the three
principal factors had an eigenvalue greater than one (>1.0), which was associated with the
cumulative variability of 52.27% of the total variance in the hydrological database. Varimax
rotation was applied to better interpret the result [
23
]. Therefore, only one principal
factor was chosen to be transformed by varimax rotation because it was the only principal
Water 2023,15, 2121 8 of 26
factor that had an eigenvalue of more than one (>1.0). Another two principal factors with
an eigenvalue less than one (<1.0) were neglected to avoid redundancy with the main
factor [34,35].
Water 2023, 15, x FOR PEER REVIEW 8 of 27
variable from this analysis was selected and applied to design the FRI model for the flood
warning system, which could serve as a useful tool in flood preparation and management.
Figure 2 shows the diagram of the scree plot used to evaluate the cut-off point of the
strong factors selected for interpretation. The diagram shows that only one of the three
principal factors had an eigenvalue greater than one (>1.0), which was associated with the
cumulative variability of 52.27% of the total variance in the hydrological database. Vari-
max rotation was applied to better interpret the result [23]. Therefore, only one principal
factor was chosen to be transformed by varimax rotation because it was the only principal
factor that had an eigenvalue of more than one (>1.0). Another two principal factors with
an eigenvalue less than one (<1.0) were neglected to avoid redundancy with the main fac-
tor ([34,35].
Figure 2. Scree plots for the cut-off point of Factor Analysis (FA).
The findings of the factor loading after varimax rotation are shown in Table 2 below.
Two factor loadings were obtained from the rotation, and these factor loadings repre-
sented 52.27% of the data cumulative variability. In this study, only a factor loading
greater than 0.70 (>0.70) was selected for interpretation as this value was considered a
stable and strong loading [24,35]. As a result, the total variability in the first factor loading
(F1) was approximately 48.68%, with large positive factor loadings for water level (0.98)
and stream flow (0.97), while factor loadings for suspended sediment (0.18) and rainfall
(0.07) were noticeably small. However, in the second factor loading (F2), all hydrological
variables had small factor loadings and the percentage of the variability in F2 was also
very small, at 3.59% of the total variability.
Table 2. Factor loading after varimax rotation for hydrological data.
Variable
F1
F2
Water Level (m)
0.981 *
0.002
Stream Flow (m3/s)
0.973 *
0.073
Rainfall (mm)
0.073
−0.110
Suspended Sediment (Tons/Day)
0.179
−0.355
Eigenvalue
1.947
0.144
Variability (%)
48.675
3.591
Cumulative (%)
48.675
52.267
Cronbach’s alpha
0.977
0.102
Note: * Factor loading more than 0.70 (>0.70).
Figure 2. Scree plots for the cut-off point of Factor Analysis (FA).
The findings of the factor loading after varimax rotation are shown in Table 2below.
Two factor loadings were obtained from the rotation, and these factor loadings represented
52.27% of the data cumulative variability. In this study, only a factor loading greater
than 0.70 (>0.70) was selected for interpretation as this value was considered a stable and
strong loading [
24
,
35
]. As a result, the total variability in the first factor loading (F1) was
approximately 48.68%, with large positive factor loadings for water level (0.98) and stream
flow (0.97), while factor loadings for suspended sediment (0.18) and rainfall (0.07) were
noticeably small. However, in the second factor loading (F2), all hydrological variables
had small factor loadings and the percentage of the variability in F2 was also very small, at
3.59% of the total variability.
Table 2. Factor loading after varimax rotation for hydrological data.
Variable F1 F2
Water Level (m) 0.981 * 0.002
Stream Flow (m3/s) 0.973 * 0.073
Rainfall (mm) 0.073 −0.110
Suspended Sediment (Tons/Day) 0.179 −0.355
Eigenvalue 1.947 0.144
Variability (%) 48.675 3.591
Cumulative (%) 48.675 52.267
Cronbach’s alpha 0.977 0.102
Note: * Factor loading more than 0.70 (>0.70).
In addition, Cronbach’s alpha was also utilised to analyse the reliability or the internal
consistency of variables for each factor. The analysis indicated that Cronbach’s alpha for
F1 and F2 was 0.98 and 0.10, respectively. The minimum acceptable value for Cronbach’s
alpha was 0.70 [
36
]. Hence, the values of the variables of factor loadings in F1 were highly
reliable and acceptable based on the internal consistency of the factor. However, the values
for F2 were excluded from being discussed further as the value of Cronbach’s alpha was
very small.
Water 2023,15, 2121 9 of 26
Furthermore, FA also revealed the interrelationship or correlation between each vari-
able and the underlying factors (Figure 3). The water level had the highest positive correla-
tion of 0.97 with F1 and other variables, followed by stream flow with a very high positive
correlation of around 0.94, suspended sediment with a very low positive correlation of
approximately 0.12, and rainfall with the markedly lowest positive correlation of 0.07. The
correlation between hydrological variables and F2 showed that only stream flow had a low
positive correlation of 0.32, while suspended sediment had a moderate negative correlation
of
−
0.59. The rainfall and water level had very low negative correlations of
−
0.19 and
−0.12, respectively.
Water 2023, 15, x FOR PEER REVIEW 9 of 27
In addition, Cronbach’s alpha was also utilised to analyse the reliability or the inter-
nal consistency of variables for each factor. The analysis indicated that Cronbach’s alpha
for F1 and F2 was 0.98 and 0.10, respectively. The minimum acceptable value for
Cronbach’s alpha was 0.70 [36]. Hence, the values of the variables of factor loadings in F1
were highly reliable and acceptable based on the internal consistency of the factor. How-
ever, the values for F2 were excluded from being discussed further as the value of
Cronbach’s alpha was very small.
Furthermore, FA also revealed the interrelationship or correlation between each var-
iable and the underlying factors (Figure 3). The water level had the highest positive cor-
relation of 0.97 with F1 and other variables, followed by stream flow with a very high
positive correlation of around 0.94, suspended sediment with a very low positive correla-
tion of approximately 0.12, and rainfall with the markedly lowest positive correlation of
0.07. The correlation between hydrological variables and F2 showed that only stream flow
had a low positive correlation of 0.32, while suspended sediment had a moderate negative
correlation of −0.59. The rainfall and water level had very low negative correlations of
−0.19 and −0.12, respectively.
Figure 3. Correlation between hydrological variables and factors.
FA findings indicated that the water level variable had the largest loadings and the
highest positive correlation with other variables and factors. This shows water level was
the strongest variable that was associated with and contributed to the flood, and was the
underlying latent factor in the study. The variable was considered the most significant
variable that was an indicator of flooding occurrence in the river. Hence, it was selected
for the development of the FRI model.
3.1.3. FRI Model
Water level values were transformed to time series analysis using SPC to compute
the limitation for the selected variable for the flood control warning system. The main
purpose of this analysis was to evaluate the efficacy of the SPC analysis in determining
the control limit for the selected variable involved in this study. The control chart for the
Figure 3. Correlation between hydrological variables and factors.
FA findings indicated that the water level variable had the largest loadings and the
highest positive correlation with other variables and factors. This shows water level was
the strongest variable that was associated with and contributed to the flood, and was the
underlying latent factor in the study. The variable was considered the most significant
variable that was an indicator of flooding occurrence in the river. Hence, it was selected for
the development of the FRI model.
3.1.3. FRI Model
Water level values were transformed to time series analysis using SPC to compute the
limitation for the selected variable for the flood control warning system. The main purpose
of this analysis was to evaluate the efficacy of the SPC analysis in determining the control
limit for the selected variable involved in this study. The control chart for the selected
variable was used to monitor the real-time water level series and identify any alarming
readings that exceeded the normal water level of the Kuantan River.
Figure 4illustrates the SPC control chart of the time series analysis of the water level
based on the individual moving range. The highest water level in the river was 24.69 m
(observation: 68900), which was in 1994. This was followed by 23.52 m in 1993 (observation:
61014), 23.32 m in 1998 (observation: 105021), and 23.08 m in 1991 (observation: 43563). All
Water 2023,15, 2121 10 of 26
these observations were recorded in the 20th century. By comparison, in the 21st century,
the highest reading was observed in 2012, at 22.87 m (observation: 219024), followed by
22.54 m (observation: 211664) in 2011, 22.21 m (observation: 235624) in 2013, and 22.17 m
(observation: 263176) in 2017.
Water 2023, 15, x FOR PEER REVIEW 10 of 27
selected variable was used to monitor the real-time water level series and identify any
alarming readings that exceeded the normal water level of the Kuantan River.
Figure 4 illustrates the SPC control chart of the time series analysis of the water level
based on the individual moving range. The highest water level in the river was 24.69 m
(observation: 68900), which was in 1994. This was followed by 23.52 m in 1993 (observa-
tion: 61014), 23.32 m in 1998 (observation: 105021), and 23.08 m in 1991 (observation:
43563). All these observations were recorded in the 20th century. By comparison, in the
21st century, the highest reading was observed in 2012, at 22.87 m (observation: 219024),
followed by 22.54 m (observation: 211664) in 2011, 22.21 m (observation: 235624) in 2013,
and 22.17 m (observation: 263176) in 2017.
Figure 4. Statistical Process Control (SPC) for water level (m) in the Kuantan River, Malaysia.
In addition, SPC analysis was used to compare the current flood alert system applied
by the Malaysian DID with the flood risk model in this study (Figure 4). According to
SPC, the UCL for the water level in the Kuantan River was 17.18 m, the CCL was 17.10 m,
and the LCL was 17.07 m. The capacity of the river to support water levels was within the
CCL range of 17.10 m and LCL range of 17.07 m. The water level capacity was unable to
sustain the river beyond the UCL range of 17.18 m. The current rates applied by the DID
in the flood warning system are 17.00 m for the normal level, 20.00 m for the alert level,
20.75 m for the warning level, and 21.50 m for the danger level.
The FRI was generated using a combination of the algebra method to verify its effi-
cacy and practicability in monitoring flood disasters. Based on SPC analysis, the water
level control limit was used to develop the FRI formula (Equation (4)). The risk of flooding
was categorised according to the high-risk, moderate-risk, and low-risk levels (Figure 5).
FRI values ranged from 0 to 100, with a high-risk rate of 70 and above, a moderate-risk
rate of 35 to 69, and a low-risk rate of 0 to 34. The risk setting for the high-risk level corre-
sponded to the values above the UCL line in the control chart of the SPC analysis. The
UCL was determined to be an intolerable value, indicating a high-risk flood event. Mod-
erate risk was determined for the FRI values plotted between the CCL line and the UCL
line, while the low-risk level was based on the FRI values plotted between the CCL line
and the LCL line. The result in Figure 6 reveals that 29.23% of the total data plotted were
classified as the high-risk class and 70.77% as the moderate-risk class. There were no val-
ues plotted in the low-risk class. The values of the high-risk class were mostly allocated
after 1991, which explains the river’s high rate of flooding in recent years.
Figure 4. Statistical Process Control (SPC) for water level (m) in the Kuantan River, Malaysia.
In addition, SPC analysis was used to compare the current flood alert system applied
by the Malaysian DID with the flood risk model in this study (Figure 4). According to SPC,
the UCL for the water level in the Kuantan River was 17.18 m, the CCL was 17.10 m, and
the LCL was 17.07 m. The capacity of the river to support water levels was within the CCL
range of 17.10 m and LCL range of 17.07 m. The water level capacity was unable to sustain
the river beyond the UCL range of 17.18 m. The current rates applied by the DID in the
flood warning system are 17.00 m for the normal level, 20.00 m for the alert level, 20.75 m
for the warning level, and 21.50 m for the danger level.
The FRI was generated using a combination of the algebra method to verify its efficacy
and practicability in monitoring flood disasters. Based on SPC analysis, the water level
control limit was used to develop the FRI formula (Equation (4)). The risk of flooding was
categorised according to the high-risk, moderate-risk, and low-risk levels (Figure 5). FRI
values ranged from 0 to 100, with a high-risk rate of 70 and above, a moderate-risk rate of
35 to 69, and a low-risk rate of 0 to 34. The risk setting for the high-risk level corresponded
to the values above the UCL line in the control chart of the SPC analysis. The UCL was
determined to be an intolerable value, indicating a high-risk flood event. Moderate risk
was determined for the FRI values plotted between the CCL line and the UCL line, while
the low-risk level was based on the FRI values plotted between the CCL line and the LCL
line. The result in Figure 6reveals that 29.23% of the total data plotted were classified as
the high-risk class and 70.77% as the moderate-risk class. There were no values plotted in
the low-risk class. The values of the high-risk class were mostly allocated after 1991, which
explains the river’s high rate of flooding in recent years.
Water 2023,15, 2121 11 of 26
Water 2023, 15, x FOR PEER REVIEW 11 of 27
Figure 5. Flood Risk Index (FRI) of the Kuantan River in Malaysia.
Figure 6. The percentage of the FRI classification for Malaysia’s Kuantan River
3.1.4. Prediction Performance by ANN
The prediction of flood risk that was aligned with the FRI was identified to set and
guide good mitigating measures to prevent and manage floods in the study area. ANN
analysis was performed on the expected precision and accuracy of the risk index infor-
mation obtained. The results in Table 3 present the prediction performance for the training
and validation of the water level. The R2 value demonstrates a result of training and vali-
dating with the lowest RMSE of 0.002855 and a total number of five hidden nodes to
achieve optimal results. The results for the training prediction of the water level showed
that R2 was 0.999937 with the lowest RMSE of 0.002855 and three hidden nodes to achieve
optimal results. The validation prediction was also carried out with a very high R2 for
water level, 0.999953, and the lowest RMSE of 0.002855, as well as five hidden nodes for
optimal results.
Figure 5. Flood Risk Index (FRI) of the Kuantan River in Malaysia.
Water 2023, 15, x FOR PEER REVIEW 11 of 27
Figure 5. Flood Risk Index (FRI) of the Kuantan River in Malaysia.
Figure 6. The percentage of the FRI classification for Malaysia’s Kuantan River
3.1.4. Prediction Performance by ANN
The prediction of flood risk that was aligned with the FRI was identified to set and
guide good mitigating measures to prevent and manage floods in the study area. ANN
analysis was performed on the expected precision and accuracy of the risk index infor-
mation obtained. The results in Table 3 present the prediction performance for the training
and validation of the water level. The R2 value demonstrates a result of training and vali-
dating with the lowest RMSE of 0.002855 and a total number of five hidden nodes to
achieve optimal results. The results for the training prediction of the water level showed
that R2 was 0.999937 with the lowest RMSE of 0.002855 and three hidden nodes to achieve
optimal results. The validation prediction was also carried out with a very high R2 for
water level, 0.999953, and the lowest RMSE of 0.002855, as well as five hidden nodes for
optimal results.
Figure 6. The percentage of the FRI classification for Malaysia’s Kuantan River.
3.1.4. Prediction Performance by ANN
The prediction of flood risk that was aligned with the FRI was identified to set and
guide good mitigating measures to prevent and manage floods in the study area. ANN
analysis was performed on the expected precision and accuracy of the risk index informa-
tion obtained. The results in Table 3present the prediction performance for the training and
validation of the water level. The R
2
value demonstrates a result of training and validating
with the lowest RMSE of 0.002855 and a total number of five hidden nodes to achieve
optimal results. The results for the training prediction of the water level showed that R
2
was 0.999937 with the lowest RMSE of 0.002855 and three hidden nodes to achieve optimal
results. The validation prediction was also carried out with a very high R
2
for water level,
0.999953, and the lowest RMSE of 0.002855, as well as five hidden nodes for optimal results.
Water 2023,15, 2121 12 of 26
Table 3.
The prediction performance of correlation of determination (R
2
) and root mean square error
(RMSE) for the FRI using Artificial Neural Network (ANN).
Prediction Model Hidden Node Train Validation
R2RMSE R2RMSE
Water Level-FRI
1 0.999918 0.002858 0.999929 0.002860
2 0.999925 0.002861 0.999934 0.002863
3 0.999937 0.002855 0.999949 0.002856
4 0.999937 0.002855 0.999949 0.002856
5 0.999937 0.002855 0.999953 0.002855
3.2. Viability of Waterborne Bacterial Diseases
3.2.1. Waterborne Diseases in Pahang, Malaysia
Waterborne diseases are one of the major flood-related issues in flood-prone areas that
have previously received little attention from researchers. This study was motivated by the
desire to demonstrate a clear link to address the issue of waterborne diseases associated
with flooding in the study area. Figure 7shows statistical data from the Malaysian Ministry
of Health (MOH) database on reported cases of patients with waterborne infectious diseases
in Pahang State from 2012 to 2017 (6 years). The data are presented to demonstrate the
risks posed to the population in the study area. The total number of confirmed cases of
waterborne infectious diseases based on the data is 4246. Bacterial food poisoning is related
to the most cases (3091 cases), followed by leptospirosis (1069 cases), dysentery (39 cases),
and melioidosis (37 cases), with typhoid or paratyphoid having the fewest cases (10 cases).
Water 2023, 15, x FOR PEER REVIEW 12 of 27
Table 3. The prediction performance of correlation of determination (R2) and root mean square
error (RMSE) for the FRI using Artificial Neural Network (ANN).
Prediction Model
Hidden Node
Train
Validation
R2
RMSE
R2
RMSE
Water Level-FRI
1
0.999918
0.002858
0.999929
0.002860
2
0.999925
0.002861
0.999934
0.002863
3
0.999937
0.002855
0.999949
0.002856
4
0.999937
0.002855
0.999949
0.002856
5
0.999937
0.002855
0.999953
0.002855
3.2. Viability of Waterborne Bacterial Diseases
3.2.1. Waterborne Diseases in Pahang, Malaysia
Waterborne diseases are one of the major flood-related issues in flood-prone areas
that have previously received little attention from researchers. This study was motivated
by the desire to demonstrate a clear link to address the issue of waterborne diseases asso-
ciated with flooding in the study area. Figure 7 shows statistical data from the Malaysian
Ministry of Health (MOH) database on reported cases of patients with waterborne infec-
tious diseases in Pahang State from 2012 to 2017 (6 years). The data are presented to
demonstrate the risks posed to the population in the study area. The total number of con-
firmed cases of waterborne infectious diseases based on the data is 4246. Bacterial food
poisoning is related to the most cases (3091 cases), followed by leptospirosis (1069 cases),
dysentery (39 cases), and melioidosis (37 cases), with typhoid or paratyphoid having the
fewest cases (10 cases).
Figure 7. Reported cases of waterborne infectious diseases in Pahang (2012–2017).
The number of bacterial food poisoning cases increased dramatically from 2014 to
2017, with a sudden spike of 1333 cases in 2016. Meanwhile, cases of leptospirosis also
gradually increased over the six years. Moreover, dysentery cases became more noticeable
in 2016. The data also revealed that there were outbreaks of melioidosis cases in 2014 and
2015, despite the prevalence of typhoid or paratyphoid disease being significantly low. As
a result, waterborne diseases became a major concern for the local population in the study
area, as they lived in the high-risk flood areas, and the population will be at risk in the
future while dealing with this issue.
Figure 7. Reported cases of waterborne infectious diseases in Pahang (2012–2017).
The number of bacterial food poisoning cases increased dramatically from 2014 to
2017, with a sudden spike of 1333 cases in 2016. Meanwhile, cases of leptospirosis also
gradually increased over the six years. Moreover, dysentery cases became more noticeable
in 2016. The data also revealed that there were outbreaks of melioidosis cases in 2014 and
2015, despite the prevalence of typhoid or paratyphoid disease being significantly low. As
a result, waterborne diseases became a major concern for the local population in the study
area, as they lived in the high-risk flood areas, and the population will be at risk in the
future while dealing with this issue.
Water 2023,15, 2121 13 of 26
3.2.2. Waterborne Bacterial Detection and Live/Dead Discrimination
The assessment of the bacterial viability of the surface water samples of the Kuantan
River was undertaken using the FCM technique with the staining procedure using the BD
™
Cell Viability Kit, which contained two fluorescent dyes. PI, a membrane-impermeable
fluorescent dye, was applied to label dead or dying cells with damaged membranes, and
TO was applied to identify viable or live cells. Simultaneous PI and TO staining for each
water sample resulted in a reproducible and distinctive pattern of bacterial cell viability
in red fluorescence over green fluorescence plots. The electronic gating strategy was
applied to differentiate the bacterial signals of either live, injured, or dead cells, from the
background noise. Cells were finally gated on FITC-A (FL1) versus PerCP-A (FL3), which
distinctly showed the discrimination of stained cells among non-damaged viable (live)
cells, intermediate (injured) cells, and membrane-damaged (dead) cells.
Figure 8displays the dot plots together with gating zones and the contour plots
of bacterial cells for the water samples collected from the upstream, midstream, and
downstream of the Kuantan River during the Northeast Monsoon season. The fluorescence
intensity of viable cells for all streams was higher compared to that of injured and dead
cells, which had low intensity. Therefore, the viability pattern of live cells was determined
to be high, mainly for the midstream, which had the highest percentage of parents (98.9%)
for the subpopulation in the hierarchy, followed by the downstream (95.8%), and lastly, the
upstream (90.6%). The high number of live cells indicated the large dynamic changes in
bacterial growth in the water sample in the study area.
Water 2023, 15, x FOR PEER REVIEW 13 of 27
3.2.2. Waterborne Bacterial Detection and Live/Dead Discrimination
The assessment of the bacterial viability of the surface water samples of the Kuantan
River was undertaken using the FCM technique with the staining procedure using the
BD™ Cell Viability Kit, which contained two fluorescent dyes. PI, a membrane-imperme-
able fluorescent dye, was applied to label dead or dying cells with damaged membranes,
and TO was applied to identify viable or live cells. Simultaneous PI and TO staining for
each water sample resulted in a reproducible and distinctive pattern of bacterial cell via-
bility in red fluorescence over green fluorescence plots. The electronic gating strategy was
applied to differentiate the bacterial signals of either live, injured, or dead cells, from the
background noise. Cells were finally gated on FITC-A (FL1) versus PerCP-A (FL3), which
distinctly showed the discrimination of stained cells among non-damaged viable (live)
cells, intermediate (injured) cells, and membrane-damaged (dead) cells.
Figure 8 displays the dot plots together with gating zones and the contour plots of
bacterial cells for the water samples collected from the upstream, midstream, and down-
stream of the Kuantan River during the Northeast Monsoon season. The fluorescence in-
tensity of viable cells for all streams was higher compared to that of injured and dead cells,
which had low intensity. Therefore, the viability pattern of live cells was determined to
be high, mainly for the midstream, which had the highest percentage of parents (98.9%)
for the subpopulation in the hierarchy, followed by the downstream (95.8%), and lastly,
the upstream (90.6%). The high number of live cells indicated the large dynamic changes
in bacterial growth in the water sample in the study area.
Furthermore, Figure 9 reveals the dot plots of FCM for water samples during the non-
Northeast Monsoon season. The viable cells for all streams also indicated intense fluores-
cence. However, the results also showed that the fluorescence intensity for injured and
dead cells was becoming more prominent, indicating an increase in cell injury and death
in the stained water samples for all streams for the non-monsoon season. In the upstream,
the percentage of parents for live bacteria cells (70.2%) for the subpopulation was rela-
tively higher compared to that of the midstream (55.6%) and downstream (68.5%), but the
number of events was less than that in other streams.
Figure 8. FCM dot plots with gating zones and contour plots of bacterial populations to evaluate
the bacterial cells in water samples collected from three main water sampling stations (upstream,
midstream, and downstream) along the Kuantan River during the Northeast Monsoon season.
Figure 8.
FCM dot plots with gating zones and contour plots of bacterial populations to evaluate
the bacterial cells in water samples collected from three main water sampling stations (upstream,
midstream, and downstream) along the Kuantan River during the Northeast Monsoon season.
Furthermore, Figure 9reveals the dot plots of FCM for water samples during the
non-Northeast Monsoon season. The viable cells for all streams also indicated intense
fluorescence. However, the results also showed that the fluorescence intensity for injured
and dead cells was becoming more prominent, indicating an increase in cell injury and
death in the stained water samples for all streams for the non-monsoon season. In the
upstream, the percentage of parents for live bacteria cells (70.2%) for the subpopulation
was relatively higher compared to that of the midstream (55.6%) and downstream (68.5%),
but the number of events was less than that in other streams.
Water 2023,15, 2121 14 of 26
Water 2023, 15, x FOR PEER REVIEW 14 of 27
Figure 9. FCM dot plots with gating zones and contour plots of bacterial populations to evaluate
the bacterial cells in water samples collected from three main water sampling stations (upstream,
midstream, and downstream) along the Kuantan River during the non-Northeast Monsoon season.
3.2.3. Concentrations of Live Bacterial Population
The concentration of the bacterial population was defined as the number of live cells
per unit volume. The findings are illustrated in the interleaved bar graphs with the error
bars representing the standard error means for three water samples. Figure 10 shows the
comparison of absolute concentrations of live bacterial populations between three water
sampling stations, namely, upstream, midstream, and downstream areas, during mon-
soon and non-monsoon seasons. According to the findings, during the monsoon season,
the midstream had a significantly greater number of live bacterial cells in comparison to
the upstream (p < 0.001) and downstream (p < 0.05), with the highest average bacterial
population of approximately 599 cells/µL. The number of live bacterial cells in the up-
stream area, which was at Panching Waterfall, averaged around 71 cells/µL during the
monsoon season and was significantly lower than the number of live bacterial cells in the
downstream (p < 0.01) during the monsoon season, which was nearly 444 cells/µL.
Meanwhile, during the non-monsoon season, the concentrations of live bacteria pop-
ulations in all streams were relatively similar, as displayed in Figure 10. The highest con-
centration was downstream with 289 cells/µL, followed by midstream with 238 cells/µ L,
and the lowest was upstream with 122 cells/µ L. There were no significant differences in
the concentration of live bacterial cells between all streams in the water sampling stations
during the non-monsoon season.
Figure 9.
FCM dot plots with gating zones and contour plots of bacterial populations to evaluate
the bacterial cells in water samples collected from three main water sampling stations (upstream,
midstream, and downstream) along the Kuantan River during the non-Northeast Monsoon season.
3.2.3. Concentrations of Live Bacterial Population
The concentration of the bacterial population was defined as the number of live cells
per unit volume. The findings are illustrated in the interleaved bar graphs with the error
bars representing the standard error means for three water samples. Figure 10 shows the
comparison of absolute concentrations of live bacterial populations between three water
sampling stations, namely, upstream, midstream, and downstream areas, during monsoon
and non-monsoon seasons. According to the findings, during the monsoon season, the
midstream had a significantly greater number of live bacterial cells in comparison to
the upstream (p< 0.001) and downstream (p< 0.05), with the highest average bacterial
population of approximately 599 cells/
µ
L. The number of live bacterial cells in the upstream
area, which was at Panching Waterfall, averaged around 71 cells/µL during the monsoon
season and was significantly lower than the number of live bacterial cells in the downstream
(p< 0.01) during the monsoon season, which was nearly 444 cells/µL.
Meanwhile, during the non-monsoon season, the concentrations of live bacteria popu-
lations in all streams were relatively similar, as displayed in Figure 10. The highest con-
centration was downstream with 289 cells/
µ
L, followed by midstream with 238 cells/
µ
L,
and the lowest was upstream with 122 cells/
µ
L. There were no significant differences in
the concentration of live bacterial cells between all streams in the water sampling stations
during the non-monsoon season.
In this study, the concentrations of the live bacterial population were also compared
between monsoon and non-monsoon seasons for all three streams of the river (Figure 11).
The concentration of the live bacterial population in the midstream was significantly
increased by 2.5-fold during the monsoon season compared to the non-monsoon season
(
p< 0.01)
. The monsoon season had a high number of live bacteria in the river in comparison
to the non-monsoon season, except for the upstream in the monsoon season, which was
lower by 0.5-fold than in the non-monsoon season. The result also showed that the live
bacteria population in the monsoon season for the downstream was approximately 1.5-fold
more than that in the non-monsoon season. However, there were no statistically significant
differences in the concentration of live bacterial cells between the monsoon season and the
non-monsoon season for upstream and downstream areas. In general, the FCM finding
revealed that the concentration of the live bacteria population was significantly higher in
the midstream during the monsoon season.
Water 2023,15, 2121 15 of 26
Water 2023, 15, x FOR PEER REVIEW 15 of 27
Figure 10. Comparison of absolute concentrations of live bacterial population in upstream (Panch-
ing Waterfall), midstream (Kuantan Medan Ferry), and downstream (Tanjung Lumpur) surface wa-
ter samples of the Kuantan River. One-way analysis of variance (ANOVA). Midstream–monsoon vs
downstream–monsoon (* p < 0.05); upstream–monsoon vs. downstream–monsoon (** p < 0.01); up-
stream–monsoon vs midstream–monsoon (*** p < 0.001).
In this study, the concentrations of the live bacterial population were also compared
between monsoon and non-monsoon seasons for all three streams of the river (Figure 11).
The concentration of the live bacterial population in the midstream was significantly in-
creased by 2.5-fold during the monsoon season compared to the non-monsoon season (p
< 0.01). The monsoon season had a high number of live bacteria in the river in comparison
to the non-monsoon season, except for the upstream in the monsoon season, which was
lower by 0.5-fold than in the non-monsoon season. The result also showed that the live
bacteria population in the monsoon season for the downstream was approximately 1.5-
fold more than that in the non-monsoon season. However, there were no statistically sig-
nificant differences in the concentration of live bacterial cells between the monsoon season
and the non-monsoon season for upstream and downstream areas. In general, the FCM
finding revealed that the concentration of the live bacteria population was significantly
higher in the midstream during the monsoon season.
Figure 10.
Comparison of absolute concentrations of live bacterial population in upstream (Panching
Waterfall), midstream (Kuantan Medan Ferry), and downstream (Tanjung Lumpur) surface water
samples of the Kuantan River. One-way analysis of variance (ANOVA). Midstream–monsoon vs.
downstream–monsoon (* p< 0.05); upstream–monsoon vs. downstream–monsoon (** p< 0.01);
upstream–monsoon vs midstream–monsoon (*** p< 0.001).
Water 2023, 15, x FOR PEER REVIEW 16 of 27
Figure 11. Comparison of absolute concentrations of live bacterial populations between the mon-
soon season and the non-monsoon season at Kuantan River. Upstream (Panching Waterfall); mid-
stream (Kuantan Medan Ferry); downstream (Tanjung Lumpur). Unpaired t-test. Monsoon–mid-
stream vs non-monsoon–midstream (** p < 0.01).
4. Discussion
4.1. Flood Risk Patterns
The flood risk patterns for the FRI model in this study was developed using chemo-
metrics. Chemometrics is a powerful environmental analytical tool based on multivariate
statistical data modelling to analyse and interpret a large and complex environmental da-
tabase [35,37]. This technique has been widely applied to study various environmental
elements in the environment because a large and complex database can reveal and pro-
duce a large amount of important information [35,38,39]. Chemometrics also aims to as-
sess relevant patterns and variations without having to be concerned about misinterpret-
ing environmental data.
4.1.1. The Most Significant Variable Contributing to Flood Occurrences
The water level variable was identified as having the strongest factor loading in the
FA findings. This was followed by the stream flow variable as the second strongest factor
loading in the analysis. This indicated that every increment of stream flow in the river
basin leads to a significant rise in water level. The discharge and velocity of the river in-
creases the capacity of the river, as more water is added either through rainfall, snowmelt,
or tributary streams, or from the groundwater seeping into the river, resulting in flooding
[40,41]. In addition, the increased rate of water flow influences the rate of erosion and the
suspended sediment yield along the river [42,43].
These findings of the FA were similar to those from a previous study [44], as the
positive factor loading of the water level changes significantly with the increasing rate of
stream flow in the Klang River Basin. Changes in stream flow, which depend on the
amount of rainfall and load of suspended sediment flowing into the river, should have an
effect on the water level in the river basin [44]. The water level and stream flow are influ-
enced by the impact of unsustainable development, which causes the river to become shal-
lower due to the massive erosion of the river bank [44]. As a result, the shallow river was
unable to accommodate a higher-than-normal volume of water, causing the river to over-
flow and causing flooding in the area, thereby inevitably affecting resident settlements.
The results also reflected the contribution of point and non-point sources to the rate of
Figure 11.
Comparison of absolute concentrations of live bacterial populations between the monsoon
season and the non-monsoon season at Kuantan River. Upstream (Panching Waterfall); midstream
(Kuantan Medan Ferry); downstream (Tanjung Lumpur). Unpaired t-test. Monsoon–midstream vs.
non-monsoon–midstream (** p< 0.01).
Water 2023,15, 2121 16 of 26
4. Discussion
4.1. Flood Risk Patterns
The flood risk patterns for the FRI model in this study was developed using chemo-
metrics. Chemometrics is a powerful environmental analytical tool based on multivariate
statistical data modelling to analyse and interpret a large and complex environmental
database [
35
,
37
]. This technique has been widely applied to study various environmental
elements in the environment because a large and complex database can reveal and produce
a large amount of important information [
35
,
38
,
39
]. Chemometrics also aims to assess
relevant patterns and variations without having to be concerned about misinterpreting
environmental data.
4.1.1. The Most Significant Variable Contributing to Flood Occurrences
The water level variable was identified as having the strongest factor loading in the
FA findings. This was followed by the stream flow variable as the second strongest factor
loading in the analysis. This indicated that every increment of stream flow in the river basin
leads to a significant rise in water level. The discharge and velocity of the river increases the
capacity of the river, as more water is added either through rainfall, snowmelt, or tributary
streams, or from the groundwater seeping into the river, resulting in flooding [
40
,
41
]. In
addition, the increased rate of water flow influences the rate of erosion and the suspended
sediment yield along the river [42,43].
These findings of the FA were similar to those from a previous study [
44
], as the
positive factor loading of the water level changes significantly with the increasing rate
of stream flow in the Klang River Basin. Changes in stream flow, which depend on the
amount of rainfall and load of suspended sediment flowing into the river, should have
an effect on the water level in the river basin [
44
]. The water level and stream flow are
influenced by the impact of unsustainable development, which causes the river to become
shallower due to the massive erosion of the river bank [
44
]. As a result, the shallow river
was unable to accommodate a higher-than-normal volume of water, causing the river to
overflow and causing flooding in the area, thereby inevitably affecting resident settlements.
The results also reflected the contribution of point and non-point sources to the rate of
suspended sediment, which results in an increase in the water level in most Malaysian
river basins [14,41,45].
However, the results also revealed that every increase in water level and stream flow
in the study area had only a small impact on suspended sediment and rainfall variables
as these two variables did not have strong factor loadings. The reduction in suspended
sediment and rainfall in the river basin did not have a significant effect on changes in water
level and stream flow in the study area. Theoretically, stream flow is monitored based on
the volume of the stream, with changes in water level serving as an indicator [
46
]. The
stream flow rate is determined by two major groups of factors, which are meteorological
factors and geomorphological factors, such as land use, soil type, and drainage, which
affect runoff [
40
]. Human development along the river is one of the contributing factors
that causes the high rate of surface runoff, which affects the stream flow and water level.
The state of uncontrolled development triggers excessive impervious surface runoff and
boosts water levels, resulting in an elevated flood risk in the study area. This is supported
by a review study on urban development and its impacts on hydrological and water quality
dynamics [47], as well as a study on the effects of urbanisation on runoff changes [48].
Furthermore, the results showed that rainfall is not the only factor that changes water
levels and triggers the risk of flooding in the study area. Many previous hydrological studies
have taken rainfall into account when referring to flood issues [
49
–
52
]. Nevertheless, rainfall
conditions are random and non-localised, and the locations of rainfall monitoring stations
are dispersed, resulting in the unsuccessful measurement of rainwater for every rainfall
event in the study area. The state of the unbalanced distribution of rainfall is influenced
by the extensive scale of atmospheric circulations and anomalies of weather and climate
variability [
25
]. This finding is consistent with other studies [
24
,
53
], which discovered that
Water 2023,15, 2121 17 of 26
the monsoon season, particularly rainfall variability, is statistically incompatible with being
a factor for flood occurrence in the river basin because rainfall distributions are generally
scattered. As a result of the FA findings, which reduced the complexity of the database, the
water level was selected as the most consistent and appropriate variable to be used in the
flood patterns for the FRI model.
4.1.2. Flood Patterns for the FRI Model
Water level values that exceed the UCL indicate a high risk of flooding, and the risk was
interpreted using the FRI model, which was proven to have high predictive performance
accuracy. Based on the findings of the ANN, which used machine learning algorithms on
hydrological data from the DID database, the FRI model’s predictive performance accuracy
was greater than 95%. The FRI model for this study was sufficiently accurate to allow
it to be applied for future flood risk research as well as future predictions of the rate of
ANN application in the FRI’s new UCL for the next 30 years [
24
]. This would help with
the development of future flood risk models and provide a clearer understanding of their
accuracy from the present to the future predictions.
The application of these methods in this study has been proven in previous stud-
ies [
14
,
24
]. According to a study in the Muda River Basin, the application of SPC
appeared to be more convenient and cost-effective, as well as producing more accurate
results in improving the early warning system for flood alerts [
24
]. Hence, the findings of
the FRI derived from the SPC in this study are capable of bringing about changes in the
regulation of flood risk control in Malaysian river basins. Prompt actions would be taken
earlier and more effectively as part of the emergency response plan at the high-risk class
level, as these approaches would provide a more detailed picture in establishing precise
guidelines for flood risk levels in the river basin in order to prevent the consequences of
major flood damage and causalities.
Based on the findings, the flood event recorded in January 2017 caused massive
destruction, costing millions of ringgits, resulting in a large number of evacuees, and
destroying significant infrastructure during the worst of the disaster. Furthermore, the
impacts became more significant because the town was the state capital and served as
the state’s main administrative and economic hub for industry and tourism [
54
]. During
the monsoon season, businesses and tourism activities were severely affected due to
disruptions in communication services and road closures caused by the floods. Historical
records indicate that the district was one of Malaysia’s state capitals with the highest risk
of experienced flood events [
55
,
56
]. Furthermore, most of the midstream and downstream
parts of the river basin are low-lying swampy areas that are prone to flooding.
Floods in Malaysia frequently occur during the Northeast Monsoon season, which
happens between November and February. This monsoon season generally results in
widespread and prolonged rainfall, often lasting for several days, which causes the river to
rise above normal levels. As a result of this study, an effective flood alert system, such as the
SPC and FRI methods, should be implemented because this system could minimise the cost
of flood management during a flood event as it could be planned and implemented in the
earlier stages of the flood disaster. Furthermore, by establishing the new control limit, local
authorities and other flood-related organisations would be able to continuously monitor
flood control, flood management, and other mitigation measures in the flood-prone areas
in Malaysia.
4.2. Viability of Bacterial Waterborne Pathogens
FCM is a technique utilised for rapid and accurate quantification of both viable but
non-culturable (VBNC) and non-viable microorganisms. It was originally applied to
eukaryotic cells, and has now been adapted and readily utilised in analysing the viability,
metabolic states, and antigenic markers of bacteria in a sample. FCM allows rapid, precise,
and quantitative information on airborne and waterborne pathogens and toxins [
57
]. The
technique is immensely beneficial because it cannot only distinguish between non-biological
Water 2023,15, 2121 18 of 26
and biological particles, but it can also identify living and dead organisms. This can be
accomplished by combining FCM with live/dead stains that distinguish between live and
dead cells [32].
4.2.1. The Occurrence of Waterborne Bacterial Diseases in Pahang, Malaysia
One of the main concerns due to the impact of flooding, particularly in the high-risk
flood areas, is the transmission of water-related diseases or waterborne diseases. Cases
of waterborne diseases such as cholera, dysentery, bacterial food poisoning, leptospirosis,
melioidosis, and typhoid fever had previously been reported in Pahang. According to the
database obtained from the MOH, food poisoning caused by bacterial infection was the
most prevalent waterborne disease in Pahang State over the six-year period (
2012–2017
). In
2016, bacterial food poisoning had the highest incidence rate of 47.3 per 100,000 population
in Malaysia [
58
]. The incidence rate of food poisoning fluctuated even though cases
continued to occur every year among school students, especially involving school canteens
and residential school kitchens [59,60].
Food poisoning is characterised by the sudden onset of vomiting, diarrhoea, or other
symptoms caused by bacteria, viruses, parasites, or chemical substances that enter the body
via contaminated food or water [
17
,
60
]. The most common bacterial pathogens that can
cause food poisoning are Salmonella, Campylobacter, Enterohaemorrhagic Escherichia coli
(EHEC),and Vibrio cholera [
17
]. As water is the major transmission route, using contami-
nated water for cleaning, food processing, and irrigation purposes exposes humans to the
bacteria when they adhere to food surfaces and kitchen utensils [
59
]. The second-most
prevalent waterborne disease in this study was leptospirosis, which is the most common
zoonotic disease worldwide and is caused by the pathogenic bacteria Leptospira interrogans.
It infects humans through direct contact with the urine of animal reservoirs or contact with
contaminated soil or water [
61
]. Many studies have suggested that the monsoon season
and flooding are associated with an increased risk of leptospirosis in endemic developing
countries [61–63].
Another common waterborne infectious disease recorded is dysentery, an illness
known as bloody diarrhoea and that is frequently caused by bacteria of the Shigella
species [
17
]. Acute diarrhoea is a major public health concern, and it is strongly asso-
ciated with food hygiene and safety, as well as practises among food handlers and the
general public [
64
–
66
]. The MOH reported that the incidence rate of dysentery in the
country is low, at 0.50 per 100,000 population, and that it occurs sporadically rather than
causing an outbreak [67].
Furthermore, in 2014 and 2015, there were significant outbreaks of melioidosis cases
in Pahang. Burkholderia pseudomalleus, a Gram-negative bacillus found in soil and water in
tropical and subtropical regions, causes melioidosis through contact with contaminated soil
or water and through penetration of skin lesions or wounds [
62
,
68
]. The infected person
presents with fever, pneumonia, septicaemia, or localised skin infections. Melioidosis
became a notifiable disease in Malaysia on 9 January 2015 due to the outbreaks and fatality
cases in a rescue operation in Lubuk Yu, Pahang in 2010, and flooding in Peninsular
Malaysia from late 2014 to early 2015 [
67
]. Typhoid fever, an acute systemic enteric disease
caused by Salmonella typhi and transmitted via the faecal–oral route, is a global public
health burden, primarily in developing countries [
69
]. The incidence of typhoid cases has
decreased over the last 10 years, and the number of cases has been low and sporadic over
the years [
67
]. This disease is frequently related to food safety and hygiene, water supply,
and wastewater management [69].
4.2.2. Bacterial Detection, Live/Dead Discrimination, and Its Association with Waterborne
Diseases during Monsoon Season
The most basic approach for determining the viability of bacteria is laboratory culture
and plate-based testing, which is usually equivalent to testing the ability to form colonies
and proliferate on a solid growth medium with liquid nutrient broths. These traditional
Water 2023,15, 2121 19 of 26
techniques are time-consuming, require strict standardised counting procedures, and are
ineffective for slow-growing or VBNC organisms. However, the FCM counting technique
effectively overcomes these disadvantages as it has been proven to provide a rapid, au-
tomated, and reliable result that accurately estimates the live, dead and total bacteria in
many routine microbiology monitoring studies [70–76].
The number of viable bacterial cells in the Kuantan River was determined using the
FCM technique when combined with viability stains that easily allowed distinction be-
tween the intact-membrane and damaged-membrane bacterial cells. A viable cell possesses
three characteristics, namely, an intact membrane, the ability to reproduce, and the ability
to be metabolically active [
77
]. The number of viable cells indicates dynamic changes in
bacterial growth that can result in potential human pathogenicity. The ability to differenti-
ate between live (viable) and dead bacteria is crucial in the microbiological field because it
is vital in many applications, such as disinfection, antimicrobial therapy assessment, assess-
ment of the viability of starter cultures, and cell proliferation monitoring [
78
]. Moreover,
enumeration and differentiation of bacterial cell viability in environmental samples are
very important in tracking and preventing the spread of infectious agents [79].
The FCM findings provided a comprehensive insight into bacterial populations in the
river and allowed conclusions to be drawn about the distribution of bacterial waterborne
pathogens during the monsoon season in the study area. The live bacterial population in the
river was significantly abundant during the monsoon season compared to the non-monsoon
season. Water levels were elevated during the monsoon season, and the rising water levels
most likely led to flood events. This implies that, as water levels rose or flooding occurred,
the live bacterial population increased, increasing the probability of exposure to the health
risk of waterborne diseases. These results were consistent with the findings of [
75
], which
established that the dependency of microbial dynamics and the improvement in the overall
microbial water composition for drinking water distribution were dependent on water
levels. Even though the effects of flooding on microbial communities occur over time,
increased surface water levels are one of the factors that increases the likelihood of shifting
microbial communities affecting clean and high-quality drinking water due to surface
water contamination [75].
Increased frequency of extreme weather events, such as flooding, not only causes
infrastructure damage and significant loss of human life, but even more devastating con-
sequences emerge in the form of increased transmission, incidence, and dispersal of wa-
terborne infectious diseases [
15
,
80
]. A study of the River Thames, England, highlighted
increased detection of river-emerging microbes following a flood event, and the slow re-
covery of flooding impacts on bank filtration systems with plausible contaminant loads
was observed when extreme flooding occurred without flexible and resilient operating
regimes [
81
]. As contaminated floodwater causes contamination of surface water and
groundwater, the water supply serves as an environmental reservoir for the transmission
of infectious diseases [69].
Furthermore, during the monsoon season, the viability and the concentration of live
bacterial cells were significantly higher in the midstream compared to the downstream
and upstream areas. In this study, the midstream was located in the Kuantan city centre,
surrounded by residential and commercial areas that are considered to be flood-prone.
Inadequate solid waste management and sanitation in residential and city areas might lead
to the emergence of high levels of waterborne pathogens [
80
,
82
]. These studies emphasised
that the majority of flood victims in these areas would be exposed to microbial infections
as a result of floodwater contamination, and thereby bear the risk of waterborne diseases.
Meanwhile, residents of the fishing villages where the downstream samples were collected
would also be affected, but the risk would be reduced because the stream was close to the
South China Sea. The upstream population of live bacterial cells was the lowest as it was
located at the Panching Waterfall, a nature preserve park in Malaysia.
Several factors could influence bacterial growth and proliferation, leading to the
infection of flood-related communicable diseases such as cholera, typhoid, leptospirosis,
Water 2023,15, 2121 20 of 26
and E. coli. Following a massive flooding event, the transmission and contraction of
leptospirosis increased among those living in urbanised and densely populated areas near
water bodies and garbage accumulation areas [
63
]. Moreover, environmental factors such
as an untreated water supply and inadequate wastewater management were associated
with an outbreak of infectious diseases following a major flood in Northeastern Malaysia
in December 2014 [
69
]. These factors were strongly related to flooding and attributed to
poor drinking water, and sanitation and hygiene situations in the environment, which
thus provide favourable environmental conditions for the transmission and outbreak
of infectious diseases [
15
,
19
,
80
,
83
]. Moreover, the current study found that due to the
impacts of climate change, such as heavy rainfall and flooding, the incidence of waterborne
diseases is expected to rise significantly, along with an increase in plastic debris, particularly
complex biofilms [84].
In addition, there is a risk of cutaneous infection following exposure to contaminated
floodwater. Trauma is common in flood victims who are injured by fast-moving water, while
attempting to escape floodwaters, or during cleaning up after flooding, and this trauma
might introduce pathogens into wounds [
85
]. For instance, melioidosis is transmitted into
wounds and skin abrasions through direct contact with contaminated soil or water. The
disease is associated with a high rate of death due to the early onset of fulminant sepsis [
86
].
The incidence and mortality rates of melioidosis are relatively high in Pahang State [
68
].
Fatality cases were associated with the melioidosis outbreak in Lubuk Yu, Pahang, in
June 2010, and it was reported that heavy rains and flooding led to soil erosion, pathogen
exposure, and water contamination, particularly of stagnant water along the river bank,
thereby increasing the risk of infection [
62
]. Furthermore, the majority of melioidosis
patients were admitted during the monsoon season, with the highest individual frequency
of monthly admissions observed during November, December, January, and February,
which were higher than in other months of the year [87].
4.3. Implications for Flood Risk and Flood-Related Disease Management
Flood risk management commonly comprises four main phases, namely, prevention
or mitigation, preparedness, response, and recovery. The first phase, flood prevention or
mitigation, comprises actions taken to avoid and reduce the impact of flooding, as well as
to protect the flood-prone areas before the disaster occurs. The actions include structural
flood control measures such as the construction of dams or river dikes and levees, and
implementation of non-structural measures such as flood forecasting and warning, flood
hazard and risk management, public participation, and institutional arrangements [
88
]. The
phase of flood preparedness comprises preparations made to accomplish readiness upon
flood arrival. These preparations include developing a flood crisis plan, utilising emergency
flood warning systems, providing awareness of flood risks and their impacts, and educating
or training the public to be ready and take immediate action for a flood disaster [89].
The flood response phase includes the emergency actions taken during a flood disaster
that aim to provide assistance, protect lives, minimise economic losses, and alleviate
suffering. This phase involves actions such as the evacuation of flood victims, rescue
and relief efforts, and the provision of temporary shelters and basic necessities including
safe food supplies, clean water supplies, and medical support services [
89
,
90
]. The last
phase of flood management is flood recovery, which involves actions taken after the flood
disaster. This phase refers to the process of reviewing the flood impacts and restoring the
normal condition as quickly as possible [
90
]. Recovery actions generally include search
and relief practice, rehabilitation and moral support, financial aid, discharge support,
reconstruction of transportation facilities, and providing rapid communication to the
impacted areas [89,90].
The first two phases, prevention or mitigation and preparedness, are the most crucial,
as the effectiveness of these phases is an indicator of the next phases as well as the full
implementation of flood risk management. Good flood forecasting and warning systems
are obligatory for flood risk mitigation, and the success is dependent on the effectiveness
Water 2023,15, 2121 21 of 26
of preparedness and the level of the correct response [
11
]. Information regarding flood
hazard risk, flood forecasting, flood monitoring, and flood zoning should be designed
to be proactive and interactive in dealing with flood disasters [
89
,
91
]. The utilisation of
a variety of effective communication channels and technological advances to promote
flood risk communication would improve community agility and resilience to the flood
disaster challenges [89,90,92].
The application of chemometrics techniques and the FRI model in this study would
enhance and strengthen the flood forecasting and warning system. The FRI model, as a part
of flood risk assessment tools, could be potentially further developed and integrated into
the flood mapping tools, such as flood hazard and flood risk maps. The flood hazard map is
an important tool to understand the hazard situation in an area by showing the extent and
expected water level or depth of a flooded area [
93
]. Meanwhile, the flood risk map would
demonstrate a combination of the probability of a flood event and the possible adverse
effects on human health, the environment, and the socio-economic factors associated with
the flood [
93
]. Contours reflecting the severity of the flood risk could be constructed based
on the FRI analysis, flood hazard information, and the database showing the viability of
waterborne bacterial diseases in flood-prone areas.
Flooding causes sudden changes in the environment as well as in human and animal
behaviour. Complex microbial communities could be highly responsive to environmental
changes [
94
,
95
]. Flooding provides the ideal environment for bacterial proliferation due
to temporary water accumulation, contamination of drinking water, which is a plausible
route of transmission, and possible disruption of routine health facilities, leading to poor or
delayed health services [
61
]. Water supply issues related to flooding, such as contamination
of water resources, scarcity of safe drinking water, outbreaks of waterborne disease, and
disruption of water treatment facilities, suggest that water supply management during
flooding should be carried out efficiently and systematically to ensure adequate and safe
water supply for flood victims [96].
In this study, the flood patterns based on the water level was found to be associated
with the bacterial population in the river of the study area. As the frequency of floods
in Malaysia increases, the incidence of waterborne infectious diseases will also intensify.
Hence, the risk of waterborne infection is present at any time a flood event occurs. In order
to mitigate and minimise the waterborne disease risk, flood mitigation measures should be
taken in advance as flooding plays a vital role in the outbreak and transmission of water-
borne diseases [
19
]. Furthermore, this serves as an early indication for future preparedness
and allocation of public health interventions in flood-affected areas to improve infectious
disease surveillance and reduce the incidence and outbreaks of waterborne disease [63].
4.4. Limitations of the Study
There were a few limitations of this study. The flood patterns and FRI model were
developed entirely using the DID’s four main hydrological data sets. In addition, nat-
ural disasters, in this case, flooding, differ because of variations that are influenced by
topography, geomorphology, structural engineering, and climate change due to global
warming. Furthermore, land use was not integrated and utilised in this study to determine
the relationship of land use with changes in suspended sediment as it was not parallel to
the findings in this study. Finally, numerous water pathogens have the potential to cause
waterborne diseases. This study, however, was limited to the flood-related water infectious
diseases caused by bacteria. The FCM application was utilised for the viability of bacterial
detection and its live or dead discrimination.
4.5. Future Research
An effective and integrated flood management system with active multi-sectoral
collaboration among government authorities and agencies is critical to preventing and
controlling waterborne diseases during the monsoon season. Further investigation of the
flood risk model might improve its practicality and effectiveness in the future. Moreover,
Water 2023,15, 2121 22 of 26
flood warning and forecasting systems should be integrated with land use and engineering
methods so that flood control can be structured more systematically and early action
can be taken promptly. In addition, future studies should explore a diverse range of
microorganisms found in floodwater, such as viruses, bacteria, and protozoans, as exposure
to pathogenic microorganisms is harmful to human health.
5. Conclusions
The water level variable was the most significant variable for the formation of the
flood risk model in the study area, as FA showed it had the strongest factor loading among
the hydrological variables. The SPC analysis emphasised the flood patterns visualisation
and the maximum limit of flood control in the river basin. Water level values beyond
the UCL in the FRI implied a high risk of flooding, and the risk evaluated using the
ANN was statistically proven, with a very strong predictive performance accuracy of over
99%. The formation of an effective control limit that was sensitive to changes in water
level and the reliability of the FRI model could be applied for future flood risk analysis,
thus strengthening existing flood warning systems. Meanwhile, FCM was found to be a
powerful and useful technique for determining the viability of bacterial populations and
the distribution of waterborne pathogens during the monsoon season. The live bacterial
population was significantly abundant in the river during the monsoon season, as rising
water levels or flooding increased the bacterial population, increasing the likelihood of
exposure to waterborne diseases. Flood victims who lived in the midstream, which is
surrounded by urbanised and densely populated areas, were significantly vulnerable to
infection due to the favourable environmental conditions for the transmission and outbreak
of waterborne diseases.
Author Contributions:
Conceptualization, A.S.M.S. and J.C.P.; methodology, N.Z.S., A.S.M.S. and
J.C.P.; software, A.S.M.S. and M.H.N.M.; validation, M.K.A.K., I.F.A. and N.S.; formal analysis, N.Z.S.
and M.H.N.M.; investigation, N.Z.S.; resources, A.S.M.S. and J.C.P.; data curation, N.Z.S.; writing—
original draft preparation, N.Z.S.; writing—review and editing, N.Z.S., A.S.M.S., J.C.P. and M.K.A.K.;
visualization, N.Z.S.; supervision, A.S.M.S., J.C.P., I.F.A., N.S. and M.K.A.K.; project administration,
A.S.M.S. and N.Z.S.; funding acquisition, A.S.M.S. and N.S. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by the Malaysian Ministry of Higher Education (MoHE) for the
Fundamental Research Grant Scheme (FRGS), grant number FRGS/1/2022/WAB02/UNIKL/02/1.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available in this article.
Acknowledgments:
The authors gratefully acknowledge Universiti Kuala Lumpur (UniKL) for
giving permission to utilise the research facilities, advice, guidance, and support for this research.
Special thanks to the Malaysian Department of Irrigation and Drainage (DID) and the Ministry of
Health (MOH) for their cooperation in obtaining data for the hydrology and waterborne diseases
cases, respectively. A special appreciation is expressed to the Immunology Unit of the Faculty of
Medicine and Health Sciences, Universiti Putra Malaysia (UPM), for the assistance in working and
training in flow cytometry.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
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