Statistical analysis of flood risk perception: a case study for Eastern Black Sea Basin, Turkey

Abstract

It is very essential in terms of flood risk management to consider social expectations such as risk perception, flood awareness, preparedness, and socio-economic dynamics together with engineering designs. Understanding the way people perceive flood risk can enhance our capability of improving existing flood risk management methods, thus helps us creating disaster resilient societies. In this study, results of a questionnaire which was used for a previous study and had been administered to participants from Eastern Black Sea Region of Turkey were further investigated using statistical methods. The main aim was to understand how demographic factors such as age, gender and education level affect people’s flood risk perception. It was also desired to see that whether they were aware of the parties responsible for taking mitigation measures, or whether they know about possible flood mitigation measures or not. Using the same data with the previous study, but in addition using SPSS software to do statistical analysis, questionnaire results were investigated using convenient statistical tests for each parameter, analysis results were interpreted, and conclusions were drawn. Same tests were conducted using weight coefficients adopted using a certain methodology which is explained in the paper, in order to make a better investigation. Also, results were compared with the results of the previous study. It was seen that there were some consistencies and contradictions between the results of the previous study and this study’s results.

Full text

ORIGINAL PAPER
Received: 7 December 2023 / Accepted: 2 March 2024 / Published online: 21 April 2024
© The Author(s) 2024
Tuğçe Anılan
tugcekoc@ktu.edu.tr
Selahattin Bayram
bayram.selahattin@hotmail.com
Mahmut Cenk Sayıl
cenksyl@gmail.com
Osman Yüksek
osmanyk20@gmail.com
1 Karadeniz Technical University Civil Engineering Department, Trabzon, Türkiye
61080, Turkey
2 Bursa Uludağ University Civil Engineering Department, Bursa, Türkiye 16059, Turkey
Statistical analysis of ood risk perception: a case study for
Eastern Black Sea Basin, Turkey
TuğçeAnılan1· SelahattinBayram1· Mahmut CenkSayıl1· OsmanYüksek2
Natural Hazards (2024) 120:8743–8760
https://doi.org/10.1007/s11069-024-06548-7
Abstract
It is very essential in terms of ood risk management to consider social expectations such
as risk perception, ood awareness, preparedness, and socio-economic dynamics together
with engineering designs. Understanding the way people perceive ood risk can enhance
our capability of improving existing ood risk management methods, thus helps us creat-
ing disaster resilient societies. In this study, results of a questionnaire which was used
for a previous study and had been administered to participants from Eastern Black Sea
Region of Turkey were further investigated using statistical methods. The main aim was
to understand how demographic factors such as age, gender and education level aect
people’s ood risk perception. It was also desired to see that whether they were aware
of the parties responsible for taking mitigation measures, or whether they know about
possible ood mitigation measures or not. Using the same data with the previous study,
but in addition using SPSS software to do statistical analysis, questionnaire results were
investigated using convenient statistical tests for each parameter, analysis results were
interpreted, and conclusions were drawn. Same tests were conducted using weight coef-
cients adopted using a certain methodology which is explained in the paper, in order to
make a better investigation. Also, results were compared with the results of the previous
study. It was seen that there were some consistencies and contradictions between the re-
sults of the previous study and this study’s results.
Keywords Risk perception · Flood awareness · Demographic factors · SPSS software
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1 Introduction
Throughout the entire world, oods are one of the most frequently encountered natural
disasters and are highly destructive. Especially in Turkey, oods cause loss of life and
damages to properties as much as earthquakes do and are in the second place in terms of
destructiveness. For these reasons, it is of vital importance for societies to employ a series
of measures, called “ood risk management” to reduce the possible undesired consequences
of oods.
While ood risk management could only be consisting of some structural and non-struc-
tural measures that can be taken in order for us to mitigate ood damages, unless some
social factors aecting people’s responses before, during and after oods are not considered,
the desired mitigation might just not be achieved since it is argued that correct behavioral
responses people show might enable us to reduce ood damages at a ratio of 80% (Groth-
mann and Reusswig 2006; Santoro et al. 2022). Hence, ood risk management policies have
been evolving to a state where social factors aecting people’s responses to ood, such as
ood risk perception, awareness and preparedness are also included in addition to the tradi-
tional measures (Lechowska 2022; Santoro et al. 2022). It can be argued that the way people
perceive ood risks, level of knowledge they have on ooding, their awareness about the
results of ooding and actions they are willing to take to be prepared for the disaster are as
important as any of the structural or non-structural ood mitigation measures.
Flood risk is dened using dierent approaches in the literature. In general, it is said that
it has three main components, namely ood hazard, vulnerability, and ood risk (Oubenna-
ceur et al. 2022). Flood hazard is the possible life and property losses that might be caused
by future oods and vulnerability might be explained as the potential risk associated with
lack of mitigation measures in a region which is ood prone (Liu et al. 2022). Flood risk
can be thought of as a combined eect of all negative consequences of oods and vulner-
ability. Flood risk perception, on the other hand, is a subjective concept; it can be dened
as the way people perceive the extent of the negative consequences of oods (Bubeck et al.
2012; Becker et al. 2014). That is why it can dier from person to person, being aected by
some demographic, psychological factors, and other factors such as ood experience. It can
be said that demographic factors such as age, gender, education level and income level are
highly determinant for ood risk perception.
The perceived probability and seriousness of a threat are both factors in risk perception.
For initiating the risk-reduction process and motivating action, it is regarded as a legitimate
predictor variable (Becker et al. 2014). The perception of ood threats has gained impor-
tance among policymakers recently who are concerned with risk management and safety
issues. Bubeck et al. (2012) examines inuences on private ood mitigation behavior have
been reviewed. Risk awareness analysis and strategy design and implementation are also
crucial steps in ood risk management, as they facilitate response to ood warnings and
the development of initiatives to improve community preparedness (Bodoque et al. 2019).
Dealing with ood hazard and risk, it is important to employ methodologies that combine
knowledge from natural and social sciences, which in turn promotes the ongoing discussion
on socio-hydrology. Communities have various options available to them, including both
non-structural and structural measures, to reduce the risk of ooding (Fuchs et al. 2017;
Bera and Danek 2018; Rana et al. 2020).
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Modern ood risk management considers understanding how the public perceives risk to
be essential since it directs the creation of eective and ecient ood mitigation solutions.
In order to searched for to examine at risk perception and disaster preparedness, information
about respondents’ sociodemographic characteristics and life experiences was also gath-
ered. The majority of respondents appeared to be fairly well equipped to handle a future
ood disaster according to the overall ndings. The results of correlational and regression
analysis showed that risk perception and disaster preparedness were positively correlated
(Miceli et al. 2008; Kellens et al. 2011). Additionally, prior experiences and demographic
characteristics, followed by civil society and the inuence of public institutions, have the
greatest impact on how people perceive the risk of ooding. Given that these are variables
that can be improved, Ardaya et al. (2017) concentrated on the analysis of civil society and
governmental institutions’ inuence. To create a complete framework for ood risk, it is
ideal to consider all potential harms (direct and indirect), including the social, psychologi-
cal, and environmental eects of ood losses (Shen et al. 2020; Oubennaceur et al. 2022).
Numerous connections between the aforementioned parameters have been discovered in
earlier investigations. For instance, it has been discovered that demographics, psychometric
variables, or prior ood experiences might predict behavior, awareness, risk perception, and
the possibility of future oods (Diakakis et al. 2018; Lechowska 2018; Huang et al. 2020).
Eryılmaz Türkkan and Hırca (2021) suggests building a ood risk framework for the social
characteristics of individuals that would make ood management plans more ecient and
sustainable.
In Anılan and Yüksek (2017), in order to evaluate the way people perceive ood risk and
level of knowledge they have on ooding, a questionnaire consisting of four Yes/No and
two open-ended questions was designed and administered to over 1000 participants from
Eastern Black Sea Basin (EBSB) in Turkey. After eliminating invalid responses, a group of
data gathered from 897 participants was obtained. Using these data, ood risk perception
level of the people and their knowledge about possible ood mitigation measures and par-
ties responsible for taking them were determined. As independent variables, demographic
factors age, gender, and education level, and also ood experience and expectations were
used to determine risk perception. However only graphs were obtained, percentage distribu-
tion of responses was given, and conclusions were drawn.
In this study, which can be thought of an extension of the mentioned previous study,
it was aimed to use statistical methods to further investigate the eects of some demo-
graphic and other factors on ood risk perception and mitigation measure knowledge of
897 participants, using the data obtained in Anılan and Yüksek (2017), and also to make
a comparison between the results of this study and of Anılan and Yüksek (2017). Data set
was analyzed using dierent statistical tests through SPSS software. Independent Sample
T-test was employed to determine whether ood risk perception (FRP) level was aected
by gender, ood experience, the fact that the resident is under risk, damage expected after
a probable future ood and belief in parties which are responsible for damage mitigation.
One-way ANOVA and Post Hoc tests were conducted to see whether FRP level was aected
by age and education level; while two-way ANOVA and Post Hoc tests were used to exam-
ine the combined eects of education level and age, education level and gender, and age
and gender on FRP level. Since Likert Scale had not been employed in Anılan and Yüksek
(2017), data was digitized using dierent methods to make it possible to use it in SPSS soft-
ware. Same analyses were conducted in two ways: In one, weighting coecients were not
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Natural Hazards (2024) 120:8743–8760
used and in the other they were. A comparison of results obtained by the analyses without
and with weighting coecients was made; also a comparison of the results of this study and
of them was made.
2 Data collected and methodology
2.1 Questionnaire design
In Anılan and Yüksek (2017), a questionnaire which was administered to 897 people was
used to investigate the ood risk perception and knowledge level of participants from East-
ern Black Sea Basin (EBSB) of Turkey. Flood disasters frequently occur in the EBSB. This
region is classied as a ood basin, and it poses a serious threat to residents in the nearby
settlement area. It has a mountainous topography and high amount of precipitation. The
detailed features and maps of the basin can be found in (Anılan et al. 2020).
The questionnaire included six items, four of them being Yes/No questions, and the rest
being open ended (Table 1). In this study, it is aimed to further investigate perceptions of
ood risk and ood damage mitigation measures of those participants through statistical
methods using SPSS software. Since the questionnaire used in Anılan and Yüksek (2017)
did not employ Likert Scale, in order for the data to be used for a statistical analysis, it had
to be converted into scaled data, that is, it had to be numerical. For these reasons, to digitize
the data, an approach based on Liu and Li (2015) and Liu et al. (2018) was taken as basis.
Since demographic factors such as age, gender and education level are of utmost importance
for the determination of FRP scores, they were also considered.
With the rst four questions, it was tried to determine the participants’ ood experiences,
personal awareness, risk and concern perception and potential of coping prociency with
Yes/No questions. In the fth open-ended question given to measure the level of knowl-
edge, options stream improvement, aorestation, preventing settlement in stream beds or
relocation, infrastructure works, awareness of public and other were completed. In the 6th
open-ended question asked to measure expectations, options state, municipality, citizen,
general directorate of state hydraulic works, district governorate, governorate and other
were given. It was possible to make an explanation with the other option in questions 5 and
Questions Purpose Choice
Have you ever experienced a ood? Flood
experience
Yes/no
Do you think that your residence is under
risk?
Personal
awareness
Yes/
No
If a ood occurs, do you think that
you will experience any loss of life or
property?
Risk and con-
cern perception
Yes/
No
Do you believe that any measure may be
taken to mitigate ood damages?
Potential
of coping
prociency
Yes/
No
Which measures may be taken to mitigate
ood damages?
Knowledge
level
Open
ended
Who is responsible for the measure? Expectation Open
ended
Table 1 Determinants of ood
risk perception (N = 897)
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6. The demographic structure of the participants was determined according to age, gender
and educational status which is presented in Table 2.
2.2 Fundamental statistical analysis
In Tables 3 and 4, the fundamental statistical results and frequency analyzes of the answers
are given for the rst 4 questions, respectively. According to the answers given, it was
revealed that most of the participants had experienced (62%) oods before and considered
themselves at risk of oods (60%). In addition, a large majority of the participants think that
they will experience losses in case of any ood (90%) and that precautions can be taken to
prevent this situation (85%).
Flood
experience
Personal
awareness
Risk and
concern
perception
Potential
of coping
prociency
NValid 896 893 897 871
Miss-
ing
14 0 26
Fre-
quen-
cy
No 341 (38.0%) 354 (39.5%) 354 (17.4%) 129
(14.4%)
Yes 555 (61.9%) 539 (60.1%) 539 (82.6%) 742
(82.7%)
Table 4 Frequency analysis for
Yes/No questions (Q1-Q4)
Question Yes No
Have you ever experienced a ood? 62 38
Do you think that your residence is under risk? 60 40
If a ood occurs, do you think that you’ll experience any
loss of life or property?
90 10
Do you believe that any measure may be taken to miti-
gate ood damages?
85 15
Table 3 Fundamental statistics
(%) for Yes/No questions (1–4),
(N = 897)
Participants Number %
Age interval
15–24 139 16
25–34 189 21
35–49 261 29
50+308 34
Gender
Male 777 87
Female 120 13
Education
Elementary School 248 28
Secondary School 119 13
High School 299 33
University 231 26
Table 2 Demographics structure
of participants (N = 897)
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According to the answers to the open-ended questions, the results of which are given
in Table 5, preventing settlement in stream beds or relocation (36.9%) is seen as the most
eective method to reduce ood risk among the participants. In the second open-ended
question, which asks about the people responsible for taking measures, the state comes rst
with 45.7%.
All digitized data had a mean value of 5.37 and a standard deviation. Adopting the
“mean ± 1 standard deviation” approach, interval of the scores was determined to have the
minimum value of 3.31 and maximum value of 7.44 (Liu et al. 2018, 2022). Grouping was
made so as to the values below 3.31 were to be “low”, values above 7.44 were to be “high”,
while the values in between were to be “moderate”. Accordingly, 137 participants had low
risk perception, while 649 and 110 participants had moderate and high-risk perceptions,
respectively.
3 Hypotheses and method
Since for each parameter that might aect the FRP level, the nature of the data is dierent,
some of them required dierent types of statistical tests to be correctly analyzed. Since it
was convenient to use independent sample T-test for the investigations of the eects of gen-
der, ood experience, residence risk, expectation of loss and belief in responsible; T-tests
were performed to investigated these. One-way ANOVA was suitable for the investigation
of the eects of age and gender on FRP level, while Two-way ANOVA was convenient for
the determination of the combined eects of demographic factors age, gender, and educa-
tion level on FRP level. Hypotheses and types of tests used to evaluate them are shown in
Table 6. In each case, null hypothesis “H0” says that there is no dierence between vari-
ances, which is the parameter of interest does not aect the dependent variable which is FRP
level. On the other hand, alternative hypothesis “H1” says that the variances dier signi-
cantly, that is; variables aect the FRP level in a statistically signicant manner.
Options NPercent (%)
Stream improvement 143 13.1
Aorestation 173 15.9
Preventing settlement in stream beds or
relocation
402 36.9
Infrastructure works 41 3.8
Awareness of public 131 12.0
Other 199 18.3
Total 1089 100.0
State 526 45.7
Municipality 134 11.6
Citizen 268 23.3
General directorate of state hydraulic
works (DSI)
42 3.6
District governate 36 3.1
Governate 60 5.2
Other 85 7.4
Total 1151 100.0
Table 5 Fundamental statis-
tics for open-ended questions
(Q5-Q6)
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In order to analyze the data in the SPSS package program, it must be converted into a
software language. 4 categories were arranged for the independent variables of age and
educational status, and 2 categories were arranged for the independent variable of gender.
In classifying the dependent variables for the program, 2 categories were determined for the
rst 4 questions, which are Yes/No questions, and 6 and 7 categories were determined for
the last 2 questions respectively, which are open-ended questions. In the rst 4 questions,
‘1’ was assigned to the yes answer and ‘0’ was assigned to the no answer. In open-ended
questions, ‘1’ was assigned to the options that the participant chose for each option, and ‘0’
was assigned to the options that the participant did not choose (Table 7).
Taking the methodology of Liu et al. (2018) as the basis for the determination of FRP,
scores for each individual were calculated and categorized into three classes as “high”,
“moderate” and “low”, based on some fundamental statistical approaches (regarding mean
and standard deviation values). Each participants’ impression of ood risk was calculated
using the mean (MV) and standard deviation (SD) of the FRP scores. If the FRP score was
Table 7 Values used to digitize the data for SPSS
Age Gender Education Q1 Q2 Q3 Q4 Q5 Q6
15–24 (1) Male
(1)
Elementary
school (1)
Yes
(1)
Yes
(1)
Yes
(1)
Yes
(1)
Stream improvement
(1 − 0)
State (1 − 0)
25–34 (2) Female
(2)
Secondary
school (2)
No
(0)
No
(0)
No
(0)
No
(0)
Aorestation (1 − 0) Municipal-
ity (1 − 0)
35–49 (3) High school
(3)
Preventing settlement in
stream beds or relocation
(1 − 0)
Citizen
(1 − 0)
50+ (4) University
(4)
Infrastructure works (1 − 0) DSI* (1 − 0)
Awareness of public (1 − 0 ) District
governorate
(1 − 0)
Other (1 − 0) Governorate
(1 − 0)
Other (1 − 0)
* General directorate of state hydraulic works
Independent T-test
Is FRP aected by gender?
Is FRP aected by ood experience? (Q1)
Is FRP aected by the fact that the residence is under risk? (Q2)
Is FRP aected by expected loss of life or property? (Q3)
Is FRP aected by the belief that the responsible are taking the
necessary measures? (Q4)
One-way ANOVA, Post Hoc Test
Is FRP aected by age?
Is FRP aected by education level?
Two-way ANOVA, Post Hoc Test
Is FRP aected by a combination of education level and age?
Is FRP aected by a combination of education level and gender?
IS FRP aected by a combination of age and gender?
Table 6 Hypotheses and tests
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larger (lesser) than 1 SD from the MV, a household’s opinion of its ood risk fell into the
high (low) category, whereas other participants fell into the moderate category ‘moderate’.
In this instance, the FRP scores’ SD, MV, minimum, and maximum values were 2.06, 5.37,
0, and 12, respectively. Therefore, the ranges for participants’ perceptions of low, moderate,
and high ood risks were (0, 3.31), (3.31, 7.44), and (7.44, 12), respectively.
According to the ndings, 650 participants (72.5%) of all questioned respondents indi-
cated that there was a moderate probability of ooding. Only 110 and 137 participants, or
roughly 12.3% and 15.3% of all surveyed individuals, respectively, reported high and low
levels of ood risk perception. A statistical analysis was made using only calculated scores;
and as proposed in Ullah et al. (2020), using weighting coecients, another analysis was
performed for comparison purposes. Signicance level was selected as 0.05 (5%), meaning
that when the signicance value is lower than 5% this means there is dierence between
variances and the eects are statistically signicant, and vice versa.
4 Results
Although there are many studies about ood risk perceptions (Liu et al. 2018; Wang et al.
2018; Ullah et al. 2020; Liu et al. 2022), there is no clear consensus on whether weights
should be used in statistical approaches. Therefore, in this study, analysis was conducted
using both without weighting FRP values and weighting coecient values. In this way, it
was possible to compare results without weighing and weighting coecient analysis.
4.1 Statistical analysis without weighting coecients
4.1.1 Independent T-test results
Table 8 shows the independent sample T-test results. It is seen that signicance and two-
tailed signicance values are 0.324 and 0.411 for the variable gender, respectively. First one
being higher than 0.05 implies that the distribution is homogeneous. The fact that two-tailed
signicance value is greater than 0.05 reveals that there is no statistically signicant dier-
ence between variances; that is, independent variable gender does not aect the dependent
variable FRP level. In this case, we fail to reject to null hypothesis. When the eects of
other parameters on FRP levels are investigated in a similar manner, it can be seen that all
other independent variables aect FRP levels signicantly; that is, when ood experience,
risk of the residence, expectations of life and property loss and belief in responsible parties’
change, FRP levels also change.
It can be said that those who had past ood experiences naturally felt themselves under
higher risk, because of the fact that they had witnessed the destructive aspect of the disaster
in real life. For the third parameter, it can easily be said that when the person thinks that his/
her residence is under risk, he/she would perceive the risk as higher. It can be said that when
people expect loss of life and property in the case of a possible future ood, they might see
themselves under a higher risk. It can even be said that the opposite of this might be the case;
that is, when the risk perception is higher, they might expect more losses. It can be argued
that when people think that the responsible parties are fullling their roles properly, they
tend to feel safer, leading to a lower level of risk perception.
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4.1.2 One-way ANOVA test results
Table 9 shows the one-way ANOVA test results. It is seen that variances are homogeneous
for the parameter age. However, signicance value is 6.9% which is greater than 5%, so
we fail to reject the null hypothesis, saying that age does not aect the FRP level. When it
comes to the investigation of the eects of education level on FRP, it is seen that the homo-
geneity of variances does not exist since P-value is lower than 0.05. When signicance
value is examined, it is seen that the value is much smaller than 0.05 variances dier greatly
and there is a statistically signicant dierence between them. So, we accept the alterna-
tive hypothesis, saying that the education level indeed aects FRP level. When a further
investigation is conducted using Tukey’s test, it is seen that the dierences are between the
groups “elementary school-university” and “high school-university.” Further, Tamhane test
Is FRP
aected by
gender?
Gender NSig. Sig.
(2-tailed)
Women 120 Equal vari-
ances assumed
0.324 0.411
Man 777 Equal vari-
ances not
assumed
0.420
Is FRP
aected by
ood ex-
perience?
(Q1)
Flood
experience
NSig. Sig.
(2-tailed)
Yes 555 Equal vari-
ances assumed
0.000 0.000
No 341 Equal vari-
ances not
assumed
0.000
Is FRP
aected
by the fact
that the
residence
is under
risk? (Q2)
Personal
awareness
NSig. Sig.
(2-tailed)
Yes 539 Equal vari-
ances assumed
0.000 0.000
No 354 Equal vari-
ances not
assumed
0.000
Is FRP
aected by
expected
loss of
life or
property?
(Q3)
Risk and
concern
perception
NSig. Sig.
(2-tailed)
Yes 741 Equal vari-
ances assumed
0.000 0.000
No 156 Equal vari-
ances not
assumed
0.000
Is FRP
aected by
the belief
that the
respon-
sibles are
taking the
necessary
measures?
(Q4)
Potential
of coping
prociency
NSig. Sig.
(2-tailed)
Yes 742 Equal vari-
ances assumed
0.122 0.000
No 129 Equal vari-
ances not
assumed
0.000
Table 8 Independent T-test
results
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Natural Hazards (2024) 120:8743–8760
suggests that there are dierences between groups “elementary school-secondary school,”
“elementary school-university” and “high school-university”.
There might be numerous factors lying behind these dierences; one might argue that
those who are better educated have a better understanding of the causes and consequences
of the oods and of how to prevent the damages to the life and property, and consequently
have a lower risk perception. Another reason might be that those who are better educated
are generally the ones with higher income, so that they might think coping with the results
of the oods, especially with damage to their properties, can be easier.
4.1.3 Two-way ANOVA test results
Table 10 shows the two-way ANOVA test results. Two-way ANOVA tests were conducted
in order to investigate the combined eects of the demographic factors considered in this
study, namely age, gender and education level. It is seen that the homogeneity of variances
does exist except for the case of the combined eects of education level and gender. Since
this homogeneity of variance is violated, this test is said to be a failed one. It is seen that
parameters age and education level separately eects FRP level since the signicance val-
ues for them are 0.037 and 0.025, respectively. However, when their combined eects are
examined, it is clear that their combination does not aect FRP level since the signicance
is greater than 0.05. When the separate and combined eects of age and gender parameters
are examined, it can be seen that all signicance values are much greater than 0.05 and
thus it can be concluded that they do not have a separate or combined eect on FRP level
according to this test.
4.2 Statistical analysis with weighting coecients
4.2.1 Independent T-test results
In Ullah et al. (2020); 1, 0.8, 0.6, 0.4 and 0.2 weighting coecients were adopted for ve
dierent suggested risk perception groups, namely extremely high, high, moderate, low, and
very low. Since in our study there are three groups, namely high, moderate, and low; it is
assumed to be convenient to use weighting coecients 1, 0.6 and 0.2 for them, respectively.
From the Table 11, it can be seen that in all of the cases except where the eect of ood
experience on FRP level is examined homogeneity of variances does exist. When two-tailed
Sig. ANOVA
Sig.
Is FRP
aected
by age?
Homoge-
neity of
variances
Based on mean 0.168 0.069
Based on median 0.572
Based on median and with
adjusted df
0.572
Based on trimmed mean 0.213
Is FRP af-
fected by
education
level?
Homoge-
neity of
variances
Based on mean 0.049 0.000
Based on median 0.020
Based on median and with
adjusted df
0.020
Based on trimmed mean 0.037
Table 9 One-way ANOVA
results
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signicance values are examined, it is clearly seen that all studied items except gender does
have eects on FRP level. This was the same case for the analysis which was performed
without using weighting coecients.
4.2.2 One-way ANOVA test results
Table 12 shows one-way ANOVA test results. Homogeneity of variances is violated for
parameter education level. For both parameters, signicance values are lower than 0.05,
thus it can be said that they both aect FRP level. Further investigation (Tukey’s test,
P = 0.033; Tamhane test, P = 0.041) tells that there are dierences between age groups 25–34
and 40–64. On the other hand, for education level parameter, dierences were observed
between groups “elementary school-secondary school”, “elementary school-high school”
and “elementary school-university” according to Tukey’s test. Tamhane test revealed that
dierences observed between groups “elementary school-secondary school”, “elementary
school-high school” and “elementary school-university”.
Sig. Tests of between-
subjects eects
Is FRP
aected
by a
combina-
tion of
education
level and
age?
Homoge-
neity of
variances
Based on
mean
0.084 Sig
Based on
median
0.200 age 0.037
Based on
Median
and with
adjusted df
0.201 edu_sta 0.025
Based on
trimmed
mean
0.077 age * edu_sta 0.168
Is FRP
aected
by a
combina-
tion of
education
level and
gender?
Homoge-
neity of
variances
Based on
mean
0.038 Sig.
Based on
median
0.041 edu_sta -
Based on
median
and with
adjusted df
0.041 gender -
Based on
trimmed
mean
0.036 edu_sta*gender -
IS FRP
aected
by a
combina-
tion of
age and
gender?
Homoge-
neity of
variances
Based on
mean
0.292 Sig.
Based on
median
0.708 gender 0.718
Based on
median
and with
adjusted df
0.708 age 0.131
Based on
trimmed
mean
0.336 gender * age 0.736
Table 10 Two-way ANOVA
results
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Sig. ANOVA
Sig.
Is FRP
aected
by age?
Homoge-
neity of
variances
Based on mean 0.096 0.022
Based on median 0.196
Based on median and with
adjusted df
0.196
Based on trimmed mean 0.102
Is FRP af-
fected by
education
level?
Homoge-
neity of
variances
Based on mean 0.001 0.000
Based on median 0.002
Based on median and with
adjusted df
0.002
Based on trimmed mean 0.002
Table 12 One-way ANOVA
results
Is FRP
aected by
gender?
Gender NSig. Sig.
(2-tailed)
Women 120 Equal vari-
ances assumed
0.061 0.314
Man 777 Equal vari-
ances not
assumed
0.355
Is FRP
aected by
ood ex-
perience?
(Q1)
Flood
Experience
NSig. Sig.
(2-tailed)
Yes 555 Equal vari-
ances assumed
0.008 0.000
No 341 Equal vari-
ances not
assumed
0.000
Is FRP
aected
by the fact
that the
residence
is under
risk? (Q2)
Personal
awareness
NSig. Sig.
(2-tailed)
Yes 539 Equal vari-
ances assumed
0.300 0.000
No 354 Equal vari-
ances not
assumed
0.000
Is FRP
aected by
expected
loss of
life or
property?
(Q3)
Risk and
concern
perception
NSig. Sig.
(2-tailed)
Yes 741 Equal vari-
ances assumed
0.910 0.000
No 156 Equal vari-
ances not
assumed
0.000
Is FRP
aected by
the belief
that the
respon-
sibles are
taking the
necessary
measures?
(Q4)
Potential
of coping
prociency
NSig. Sig.
(2-tailed)
Yes 742 Equal vari-
ances assumed
0.000 0.000
No 129 Equal vari-
ances not
assumed
0.000
Table 11 Independent T test
results
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Comments made for the analysis made without weighting coecients as to why these
groups would dier from each other in their risk perception levels might be valid for this
analysis as well.
4.2.3 Two-way ANOVA test results
Table 13 shows two-way ANOVA test results. In this test, only the combined eects of
gender and age parameters were successfully tested because other two combinations, homo-
geneity of variances was violated. From the Table 13, it is seen that neither age and gender
parameters’ separate nor combined eects had any inuence on FRP level of participants.
Sig. Tests of between-
subjects eects
Is FRP
aected
by a
combina-
tion of
education
level and
age?
Homoge-
neity of
variances
Based on
mean
0.005 Sig
Based on
median
0.032 age -
Based on
median
and with
adjusted df
0.032 edu_sta -
Based on
trimmed
mean
0.013 age * edu_sta -
Is FRP
aected
by a
combina-
tion of
education
level and
gender?
Homoge-
neity of
variances
Based on
mean
0.002 Sig.
Based on
median
0.005 edu_sta -
Based on
median
and with
adjusted df
0.005 gender -
Based on
trimmed
mean
0.005 edu_sta*gender -
IS FRP
aected
by a
combina-
tion of
age and
gender?
Homoge-
neity of
variances
Based on
mean
0.039 Sig.
Based on
median
0.130 gender 0.976
Based on
median
and with
adjusted df
0.130 age 0.080
Based on
trimmed
mean
0.046 gender * age 0.710
Table 13 Two-way ANOVA
results
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5 Discussion
The results obtained as a result of the analyzes are compiled in the Table 14. It can be seen
that use of weighting coecients does not cause major dierences in FRP scores; only dif-
ference was observed while testing the hypothesis “Is FRP aected by age?” using one-way
ANOVA test. The study aimed to further investigate the results of a previous study Anılan
and Yüksek (2017), in which a questionnaire consisting of four Yes/No and two open-ended
questions administered to 897 participants from Eastern Black Sea Region of Turkey to
determine their ood risk perceptions. Dierent from the previous study, SPSS software
was used to examine the relationships between various factors and ood risk perception of
each participant.
In Anılan and Yüksek (2017), answers “yes” given to questions to determine whether
participants had had any ood experience and whether they had thought that their residence
was under risk were nearly equal, as mentioned by the authors. In consistency with this, in
our study it is clear that ood experience inuences participants’ risk perceptions. In Anılan
and Yüksek (2017), it was observed that, in general, women were more conscious about
ood damage mitigation measures, as they mentioned more sustainable and environmen-
tally friendly measures while responding to the open-ended questions. It was also observed
that many of the participants did not know who was responsible for taking measures, but in
general women were again more conscious and also aware of their self-responsibilities. It
was concluded that male participants’ risk perception was lower since they had a tendency
to expect less loss. However, in this study, it seems these dierences were not reected into
Independent T test FRP FRP (weight)
Is FRP aected by gender? No No
Is FRP aected by ood experi-
ence? (Q1)
Yes Yes
Is FRP aected by the fact that
the residence is under risk? (Q2)
Yes Yes
Is FRP aected by expected loss
of life or property? (Q3)
Yes Yes
Is FRP aected by the belief
that the responsibles are taking
the necessary measures? (Q4)
Yes Yes
One-way ANOVA, Post Hoc
Test
Is FRP aected by age? No (not
homogeneous)
Yes (homo-
geneous)
Is FRP aected by education
level?
Yes (not
homogeneous)
Yes (not ho-
mogeneous)
Two-way ANOVA, Post Hoc
Test
Is FRP aected by a combina-
tion of education level and age?
only age aects,
combination does
not (homogeneous)
not
homogeneous
Is FRP aected by a combina-
tion of education level and
gender?
not homogeneous not
homogeneous
IS FRP aected by a combina-
tion of age and gender?
no (homogeneous) no (homoge-
neous)
Table 14 Summary of statistical
results
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Natural Hazards (2024) 120:8743–8760
the participants’ ood risk perception levels because statistical analyses yielded no eects
of gender on FRP levels.
In Anılan and Yüksek (2017), age seemed to aect awareness on possible ood miti-
gation measures and parties responsible for taking them. In general, younger participants
seemed to be more aware of modern mitigation measures and responsible parties. In this
study, only the eects on FRP level were examined and seen that age aected FRP level
when the weighting coecients used in one-way ANOVA and two-way ANOVA tests. This
might arise from the dierent opinions of participants from dierent age groups on ood-
ing. In Anılan and Yüksek (2017), it was observed that people with higher education levels
mentioned more environmentally friendly solution for ood damage mitigation and were
aware of their self-responsibilities more. In this study, our ndings showed that education
level signicantly aects FRP level. This might be the result of the dierent opinions and
awareness of people having dierent educational levels.
When a comparison is made between the results of Anılan and Yüksek (2017) and this
study, there are some consistencies and disagreements. In both studies, ood experience
aected FRP levels. In Anılan and Yüksek (2017), women were observed to be more aware
of ood mitigation measures and their self-responsibilities and men had less risk perception
since they had expected less losses. However, our study found no statistically signicant
Relationship between gender and FRP. In Anılan and Yüksek (2017), it was observed that
younger people were more aware of modern mitigation measures and parties responsible for
taking them. However, in this study, only the eects of age on FRP levels were examined
and seen that age aected FRP levels when the weighting coecients used in one-way
ANOVA and two-way ANOVA tests. Also in both studies, education level seemed to have
eects on FRP levels and awareness.
Although the tting results of the model with the previous study of Anılan and Yüksek
(2017) are encouraging, this study still has some limitations. There was a signicant per-
centage of participants were men which might cause some uncertainty in the results. Future
research should be more comprehensive. Additionally, there might be other factors which
might be ignored.
6 Conclusions
In this study, results of a previous study (Anılan and Yüksek 2017) were used, a dierent
method was adopted to interpret the results. In their study, a questionnaire was administered
to over 1000 participants from Eastern Black Sea Region of Turkey, to investigate their
ood risk perception and knowledge level. The questionnaire consisted of six questions,
four of them being Yes/No questions and the rest of them being open-ended. Using the
responds, some conclusions were drawn but no statistical method was used to interpret the
responds. It was aimed to perform some statistical tests using SPSS software to adopt a dif-
ferent approach and further investigate the questionnaire results.
In Anılan and Yüksek (2017) a Likert Scale was not employed, for this reasons data had
to be converted to scaled data in order for it to be used for statistical analyses. An approach
based on Liu and Li (2015) and Liu et al. (2018) was taken as basis for this purpose. Demo-
graphic factors such as age, gender and education level were also considered since they are
known to aect risk perception and awareness of people greatly. Same statistical analyses
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Natural Hazards (2024) 120:8743–8760
were performed both with and without weight coecients to allow for a comparison of
results between them as well.
When weight coecients were not used; independent sample t-test was performed to see
whether FRP changed with gender, ood experience, risk of the residence, expectations of
life and property loss and belief in responsible parties; it was seen that only gender had no
eects on FRP, all the other parameters signicantly aected FRP levels of the participants.
It can be argued that people who had had ood experience could felt themselves under
more risk. It was also expected for residence risk to have an eect on FRP since people in
risky zones would naturally feel themselves under higher risk. It can also be said that when
people think that the responsible parties are fullling their roles properly, they tend to feel
safe and have a lower risk perception. One-way ANOVA tests were performed to investigate
the eects of age and education level on risk perception. It was seen that age did not have
any eects on FRP, while education level did. To see how FRP diered between dierent
subgroups of education level parameter, Tukey’s test was performed. It was observed that
the dierences were between “elementary school-university” and “high school-university”.
Tamhane Test was also applied and showed that there were dierences between groups “ele-
mentary school-secondary school”, “elementary school-university” and “high school-uni-
versity”. Two-way ANOVA tests were used to examine the combined eects of education
level and age, education level and gender, and age and gender. Since for the combination of
education level and gender, the homogeneity of variances was violated, these results could
not be interpreted. The other combinations did not have eects on FRP levels.
When weighting coecients were used; independent t-test results showed that except
gender, all other parameters had eects on FRP levels. This was in consistency with the
results of the same tests when weighting coecients were not used. When one-way ANOVA
was performed, it was seen that both age and education level aected FRP levels. Tukey’s
test suggested that the dierences caused by education level were between groups “ele-
mentary school-secondary school”, “elementary school-high school” and “elementary
school-university”. Tamhane Test results were in agreement with these. When two-way
ANOVA was performed, only in the case where the combined eects of gender and age the
homogeneity of variances was not violated. Thus eects of only this combination could be
examined and it was seen that both separate and combined eects of these parameters had
any inuence on FRP levels of the participants. As a results, it can be said that among the
cases where the homogeneity of variances was not violated, the only dierence between the
results of the tests where the weighting coecients were used and not used, was the eects
age on FRP. Normally age did not have any eect on FRP, but when weighting coecients
were used, it was seen that age aected FRP levels of participants.
In particular, this article has the potential for wider readership and to make contributions
to hazards professionals, GIS professionals, and those working on perception of risk among
a variety of hazard domains. It would be more complete research if sensitivity analysis or
similar were performed to see if any of the variables oered interaction or if the variables
selected were signicant in overall prediction. Based on the results of this study, training
programs should be carried out in order to increase the ood risk awareness of the people of
the region. There was a signicant percentage for whom this was not accessible that major-
ity of respondents were men. Policy makers should work on equalize participant gender. In
addition, surveys and their statistical analysis could also be done periodically to determine
the level of awareness and preparedness of the population.
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Natural Hazards (2024) 120:8743–8760
In this study, which was conducted with reference to the survey results compiled by
Anılan and Yüksek (2017), it was aimed to obtain in-depth examinations based on numer-
ous statistical studies mentioned in the literature. The results demonstrated vast parallelism
with previous studies. Overall, the ndings of the study are at a level that can give insight
and be useful in terms of ood management.
Funding The authors declare that no funds, grants, or other support were received during the preparation of
this manuscript.
Open access funding provided by the Scientic and Technological Research Council of Türkiye (TÜBİTAK).
Data availability Data and models that support the ndings of this study are available from the corresponding
author upon reasonable request.
Declarations
Competing interests The authors have no relevant nancial or non-nancial interests to disclose.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
licence, and indicate if changes were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material.
If material is not included in the article’s Creative Commons licence and your intended use is not permitted
by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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