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Landslide susceptibility mapping of the Sera River Basin using logistic regression model

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Of the natural hazards in Turkey, landslides are the second most devastating in terms of socio-economic losses, with the majority of landslides occurring in the Eastern Black Sea Region. The aim of this study is to use a statistical approach to carry out a landslide susceptibility assessment in one area at great risk from landslides: the Sera River Basin located in the Eastern Black Sea Region. This paper applies a multivariate statistical approach in the form of a logistics regression model to explore the probability distribution of future landslides in the region. The model attempts to find the best fitting function to describe the relationship between the dependent variable, here the presence or absence of landslides in a region and a set of independent parameters contributing to the occurrence of landslides. The dependent variable (0 for the absence of landslides and 1 for the presence of landslides) was generated using landslide data retrieved from an existing database and expert opinion. The database has information on a few landslides in the region, but is not extensive or complete, and thus unlike those normally used for research. Slope, angle, relief, the natural drainage network (including distance to rivers and the watershed index) and lithology were used as independent parameters in this study. The effect of each parameter was assessed using the corresponding coefficient in the logistic regression function. The results showed that the natural drainage network plays a significant role in determining landslide occurrence and distribution. Landslide susceptibility was evaluated using a predicted map of probability. Zones with high and medium susceptibility to landslides make up 38.8 % of the study area and are located mostly south of the Sera River Basin and along streams.
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1 23
Natural Hazards
Journal of the International Society
for the Prevention and Mitigation of
Natural Hazards
ISSN 0921-030X
Nat Hazards
DOI 10.1007/s11069-016-2591-7
Landslide susceptibility mapping of the
Sera River Basin using logistic regression
model
Nussaïbah B.Raja, Ihsan Çiçek, Necla
Türkoğlu, Olgu Aydin & Akiyuki
Kawasaki
1 23
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ORIGINAL PAPER
Landslide susceptibility mapping of the Sera River Basin
using logistic regression model
Nussaı
¨bah B. Raja
1
Ihsan C¸ic¸ek
1
Necla Tu
¨rkog
˘lu
1
Olgu Aydin
1
Akiyuki Kawasaki
2
Received: 25 August 2015 / Accepted: 20 September 2016
ÓSpringer Science+Business Media Dordrecht 2016
Abstract Of the natural hazards in Turkey, landslides are the second most devastating in
terms of socio-economic losses, with the majority of landslides occurring in the Eastern
Black Sea Region. The aim of this study is to use a statistical approach to carry out a
landslide susceptibility assessment in one area at great risk from landslides: the Sera River
Basin located in the Eastern Black Sea Region. This paper applies a multivariate statistical
approach in the form of a logistics regression model to explore the probability distribution
of future landslides in the region. The model attempts to find the best fitting function to
describe the relationship between the dependent variable, here the presence or absence of
landslides in a region and a set of independent parameters contributing to the occurrence of
landslides. The dependent variable (0 for the absence of landslides and 1 for the presence
of landslides) was generated using landslide data retrieved from an existing database and
expert opinion. The database has information on a few landslides in the region, but is not
extensive or complete, and thus unlike those normally used for research. Slope, angle,
relief, the natural drainage network (including distance to rivers and the watershed index)
and lithology were used as independent parameters in this study. The effect of each
parameter was assessed using the corresponding coefficient in the logistic regression
function. The results showed that the natural drainage network plays a significant role in
determining landslide occurrence and distribution. Landslide susceptibility was evaluated
using a predicted map of probability. Zones with high and medium susceptibility to
landslides make up 38.8 % of the study area and are located mostly south of the Sera River
Basin and along streams.
Keywords Landslide Susceptibility Logistic regression Geographical information
systems (GIS) Turkey
&Olgu Aydin
oaydin@ankara.edu.tr; drolguaydin@gmail.com
1
Department of Geography, Faculty of Humanities, Ankara University, 06100 Sıhhıye/Ankara,
Turkey
2
Department of Civil Engineering, The University of Tokyo, Tokyo 153-8505, Japan
123
Nat Hazards
DOI 10.1007/s11069-016-2591-7
Author's personal copy
1 Introduction
Landslides are one of the most important geohazards, causing economic and social losses
as well as damage to soil and water resources (Schlogel et al. 2015; Korup et al. 2012
Alimohammadlou et al. 2013). Worldwide, landslides account for 17 % of all deaths
caused by natural hazards (Herath and Wang 2009; Kjekstad and Highland 2009). Rapid
growth and urbanisation in the developing world as well as a changing climate are among
the main reasons for the observed global increase in landslides. In Turkey, landslides
triggered by both social and physical factors are the second most dangerous natural hazard
in terms of socio-economic losses (Demir et al. 2015a,b). US$ 80 million of property is
lost each year in Turkey due to landslides (Yalcin 2011,2007). Moreover, a considerable
amount of fertile land is carried away yearly by rivers as a result of soil erosion, which
leaves the remaining fertile land exposed to increased erosion and landslides (Irvem et al.
2007; Aydin and Tecimen 2010). Such environmental hazards have been exacerbated by
disorganised and traditional land use practices in rural areas, where planning and land
management regulations are often disregarded (Demir et al. 2015b).
Landslides are a continuing problem in the Eastern Black Sea Region, especially in the
Trabzon province where about 64 % of the region is at risk from landslides (Reis et al.
2009). From 2005 to 2008, 178 landslides occurred in areas with heavy rainfall and steep
slopes (Bayrak and Ulukavak 2009). Several landslide-related studies have been conducted
in the last decade for this region of the Black Sea (Demir et al. 2015b; Bayrak and
Ulukavak 2009; Yalcin 2007; Nefeslioglu et al. 2008; Yalcin et al. 2011; Reis et al. 2009),
with deforestation, population increase, extreme precipitation, topography, geology, river
networks and land use being some of the observed factors that contribute to landslide
activity. Widespread agriculture (hazelnuts and tea are cultivated) makes the Trabzon
province an economically important region for Turkey. Also, the region, which is located
on the legendary Silk Road, is an important hub for tourism and trade, registering an
increase in ecotourism in the past decade (Reis et al. 2009; Cavus 2014).
Landslides occur regularly in the Sera River Basin, the designated study area of this
paper. However, only a few relevant studies have been carried out in the area. The study
area is located in the western part of the Eastern Black Sea region in the county of
Akc¸aabat, southwest of the city of Trabzon, one of the most important harbour cities in
Turkey. Landslides in this region are triggered both naturally and artificially. Natural
triggers constitute heavy precipitation, stream erosion of slope toes and weathering of the
bedrock. Artificial triggers include steep and improperly cut slopes, poorly controlled
surface drainage, and uncontrolled settlement and agricultural activities. Landslide activity
is currently concentrated in one area, north of Lake Sera. Nonetheless, it is believed that
the spatial distribution of landslides is likely to increase as a result of extreme precipitation
events in the Eastern Black Sea, and severe deforestation to accommodate more agriculture
and infrastructure. For the purposes of disaster and risk management, an assessment of
landslide susceptibility in the Sera River Basin is imperative.
Landslide susceptibility can be defined as the propensity of an area to generate land-
slides (Guzzetti et al. 2006). Several techniques can be employed for hazard modelling.
These mainly fall into four categories: (1) expert evaluations, (2) statistical methods, (3)
non-deterministic models and (4) mechanical approaches (deterministic or numerical
models). While expert evaluation methods are the most common approach used to evaluate
landslide hazards (Sarkar and Anbalagan 2008; Raghuvanshi et al. 2014; Kayastha et al.
2013), the subjectivity of decision making means there is a need for more sophisticated
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techniques to be integrated into the overall methodology. A way to reduce bias is to include
statistical approaches (Fall et al. 2006). Since such methods are also relatively easy to
implement in geographical information systems (GIS) (Kawasaki et al. 2001; Pradhan
2013), an increasing number of studies have adopted statistical approaches such as
bivariate and multivariate analysis including logistic regression, for landslide susceptibility
mapping (Zhuang et al. 2015; Nefeslioglu et al. 2008; Akgun et al. 2011; Mancini et al.
2010; Althuwaynee et al. 2014; Schicker and Moon 2012; Youssef et al. 2015; Bui et al.
2011; Hina et al. 2014). Many non-deterministic models are designed to overcome the
complexity of landslide susceptibility mapping (matter-element model, fuzzy set methods,
artificial neural network, fuzzy models) and have been employed in several studies con-
cerning landslide hazard assessment (Wu et al. 2003; Bui et al. 2012; Pradhan 2011a,b;
Shahabi et al. 2012; Zare et al. 2013; Wu et al. 2014). Mechanical approaches, on the other
hand, use deterministic and/or numerical models that assess slope stability to evaluate
landslide hazards (De Vita et al. 2013; Jia et al. 2012; Armas et al. 2013; Notti et al. 2015).
Despite the effectiveness of non-deterministic and machine learning methods that consider
complex classification problems, these methods have a steep learning curve. Furthermore,
some of these methods can only be implemented using specific software.
The aim of this study was to carry out a landslide susceptibility assessment using a
statistical approach for one of the regions in Turkey most at risk from landslides: the Sera
River Basin. This area was chosen as it is at high risk of landslides and also shows high
potential for tourism and recreation by local authorities (Cavus 2014; Bayrak and Ulu-
kavak 2009). Tourist traffic to the Sera Lake and surrounding areas has led to more
settlement in the region. In addition, quarrying operations are currently being carried out
on land displaced by a landslide in 1950. The probability of landslide activity in the area
has increased considerably as a result of vibrations from these operations. In identifying the
areas sensitive to landslides, existing residential regions that are at risk and areas suit-
able for future settlement could be identified. Using data about past landslides, the study
developed a tool to produce a landslide probability map. The binary logistic regression
used in this study is one of the most popular statistical methods for determining landslide
susceptibility and has often been applied for this purpose (Suzen and Doyuran 2004;
Ercanoglu and Temiz 2011; Akgun 2012). The application of logistic regression requires
the inclusion of landslide triggering and/or conditioning parameters as independent vari-
ables. In general, the more independent variables that are included, the more complete the
model will be, given that the consideration of variables plays a major role in determining
the dependent variable. According to Coe et al. (2000) and Fabbri et al. (2003), limited
data, if of sufficiently high quality, can produce more accurate results as compared to more
information of poorer quality. The main advantage of this method is therefore its ability to
evaluate the significance of causative factors while eliminating those factors which are
unrelated (Yesilnacar and Topal 2005; Chauhan et al. 2010; Ghosh et al. 2011). The study
involved the derivation of a landslide probability equation which considers topographic,
hydrological and geological parameters by applying logistic regression analysis to the
modelling area. The equation was then validated by applying it to the whole of the Sera
River Basin. Also, while most studies carried out in Trabzon province are on the regional
scale (Yalcin et al. 2011; Reis et al. 2009), few focus on small-scale landslide hazard
mapping (Akgun and Bulut 2007; Yalcinkaya and Bayrak 2005), which is helpful for those
making administrative decisions in the region.
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2 Study area
The Sera River Basin, with an area of 73.75 km
2
, is located west of the Trabzon province,
which is in the administrative boundaries of the county of Akc¸aabat. The basin is 2 km
inland from the coast, between latitudes 40°520000–41°003000 N and longitudes 39°310000
39°390000E (Fig. 1). It lies within the Eastern Black Sea Region Watershed, part of the
Caucasus Ecologic Region, which is one of the five biologically diverse world regions as
identified by the World Wildlife Fund (WWF) and thus is of international importance
(WWF 2015).
Lake Sera, situated in the north of the basin, was formed as a result of a landslide in
1950 and is now a nature reserve and tourist destination in Turkey. The Sera Landslide
occurred on 21 February 1950, about 2.5 km inland from the coast. It resulted in about 15
million m
3
of land being displaced, making it one of the most significant landslides seen in
the Eastern Black Sea Region. A week before the event, several signs of an impending
landslide were observed, mainly in the form of long and deep cracks. The landslide was
due to intensive chemical disintegration in the geomorphological structure of the region as
a result of the presence of deep fissures in basaltic and andesitic lava formations from the
Upper Cretaceous period (Fig. 2). This disintegration caused instability and susceptibility
to moisture from higher-than-expected rainfall and the sudden melting of snow due to
Fig. 1 Location and topography of the Sera River Basin
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southern Foehn winds, which caused a significant amount of water to leak into the ground.
The landslide, 220 m from foot to crown, is believed to have stemmed from the largest
crack. The flow of land was complex, with three landslide types identified, namely single
block slides, multiple rotational block slides and topple failures. The landslide created a
wall from west to east that blocked the valley and the Sera River and led to the formation
of a dam/lake. This endorheic basin is about 4 km long, 200 m wide and 50 m deep
(Boztas 1986).
The topography of the basin has an average slope of 23°. The average precipitation in
this region is 822 mm, considerably more than that for Turkey as a whole (643 mm). As in
the rest of the Black Sea Region, subsistence/semi-subsistence farming is common, but
there is little large-scale commercial farming as a result of the topography and land
ownership issues (Inan et al. 2011; Demir et al. 2015b). Areas north and south of the lake
have seen major infrastructure development with the construction of buildings and roads.
This construction is believed to have been stimulated by tourism (Fig. 3). Ongoing soil
erosion and conversion of land for agriculture and infrastructure has resulted in an increase
in the frequency of landslides in the active landslide zone. Currently, this area is 0.12 km
2
,
but it might grow if current practices are not controlled. The last known landslide, along
the site of a new road, is believed to have occurred in March–April 2015.
Fig. 2 Cross section of the active landslide zone of the Sera River Basin Source: Sarıyılmaz (1972)
Fig. 3 Aerial view of Lake Sera. Areas north and south of the Basin have seen considerable development
leading to deforestation of the forest around thus increase landslide risks. The red line designates the extent
of the Sera River Basin and yellow the presently active landslide zone Source: Images obtained from Google
Earth
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3 Input data sources
3.1 Landslide data
The application of logistic regression for the purpose of landslide susceptibility mapping
requires a reliable inventory of the type, activity and spatial distribution of all landslides in
the study area. However, in Turkey these inventories are not common (Akgun and Bulut
2007). The existing database of landslides in the region, obtained from national catalogues,
includes only the 1950 landslide which led to the creation of Lake Sera. These data are in
polygon form and may include debris runout zones, which would lead to an overestimation
of the landslide source region. Post-1950, landslide activity in the Sera River Basin has
been continuous, occurring regularly especially over the past 5 years as a result of the
aforementioned reasons. In the absence of a complete landslide inventory, expert opinion
was employed to map landslide activity (Fig. 4). This map shows that landslides of the
slide type are the most common in the area. Because this study is based on limited data,
this landslide susceptibility assessment should be considered preliminary until a landslide
database is developed and a more advanced study is possible.
3.2 Landslide factors
Factors affecting slope stability are various and are in most cases interconnected. The main
trigger factor for landslides in the study area and most of the Black Sea region is heavy
precipitation due to the increase in precipitation extremes and intensity observed (Can et al.
Fig. 4 Landslide and model area of the Sera River Basin
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2005; Yalcin and Kavurmaci 2013). The influence of precipitation on landslides differs
depending on landslide type, dimension, kinematics and material involved. Shallow fail-
ures, often occurring in the Sera River Basin, are usually triggered by short intense storms
(Guzzetti et al. 1992; Flentje et al. 2000; Can et al. 2005). Because precipitation is rela-
tively uniform throughout the study area, it was not included in the regression analysis as
its effect is negligible. Seismicity was not considered for the same reason. River incision
and aggradation may occur as a result of tectonics and thus influence susceptibility to
landslides leading to changes in the river courses and local topography (Dunning et al.
2007; Pirasteh et al. 2009). Roads were excluded because they are few. In the eastern part
of the study area, there is a single road, running parallel to the Sera River. Land use is the
main reason for anthropologically induced instability in the Sera River Basin. Land use
pattern has been changing in this region in terms of agricultural needs and settlement area
in order to cater for the increase in population. The deforestation of the native broadleaf
forests has made this region more prone to landslides, especially in cleared and sparsely
vegetated areas. However, the unavailability of current and accurate details of land use
change in the region prevented the inclusion of this parameter. Similarly, no data were
available on weathering of the bedrock and erosion of the slope toes and other soil
properties. The spatial variation in slope stability and hydrological conditions, including
soil moisture and groundwater flow, is controlled mainly by topographic conditions. As
such, topographic indices such as the topographic wetness index (TWI) can be used to
describe spatial soil moisture patterns (Yilmaz 2009). Subsequently, the analysis consid-
ered the following landslide-conditioning parameters: profile curvature, slope, aspect,
relative relief, distance to rivers, topographic wetness index and lithology.
Supporting data for this study was obtained from 1:25,000-scale topographic maps and
1:100,000-scale geological maps prepared by the General Directorate of Mineral Research
and Exploration of Turkey. Contour lines that contain elevation values were extracted from
the topographic map, after which a digital elevation model (DEM) with cell size
25 m 925 m was constructed. Slope, aspect, relief, profile curvature and topographic
wetness index values were calculated using the DEM. Geologic maps were scanned and
then digitised in ArcGIS to prepare the lithology. All the data layers mentioned below in
the context of logistic regression analysis are shown in Fig. 5.
3.2.1 Profile curvature
Most slopes are not uniform but rather consist of a sequence of convex, concave and
uniform areas, whose effect on sediment load and erosion is not properly reflected by
overall average steepness (Di Stefano et al. 2000). The profile curvature refers to the
curvature of the land surface in the direction of the steepest slope, with respect to the
vertical plane of a flow line. The profile curvature affects the rate at which water drains
from the surface and influences erosion and deposition. Erosion is more likely to be
prominent in areas transitioning from convex (negative) and flat profile curvature to
concave (positive) curvature while deposition occurs in places with convex surfaces
(Alkhasawneh et al. 2013; Cavalli et al. 2016). Curvature was classified into three classes:
concave (\0), flat (0) and convex ([0).
3.2.2 Slope and aspect
Slope and aspect are significant conditioning parameters of small-scale landslides (Lee and
Min 2001; Dai and Lee 2002). Slope angle is used regularly in landslide susceptibility
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studies as it is one of the primary causes of landslides (Lee 2005; Yalcin 2008; Nefeslioglu
et al. 2008), while aspect is also an important factor in assessing landslide hazard sus-
ceptibility (Yalcin and Bulut 2007; Galli et al. 2008). Aspect is associated with parameters
such as exposure to sunlight, drying winds and rainfall, which may affect the location of
landslides (Suzen and Doyuran 2004). The slope and aspect parameters for the model and
validation area were extracted from the DEM. The overall slope was classified into five
Fig. 5 Data layers used in the logistic regression analysis. aSlope, baspect, cprofile curvature, drelative
relief, elithology, fdistance to river, gTWI
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classes: \10°,10°–20°,20°–30°,30°–40°and [40°. The aspect was also divided into five
classes: flat (-1°), north (0°–45°and 315°–360°), east (45°–135°), south (135°–225°) and
west (225°–315°).
3.2.3 Relative relief
Relative relief refers to the elevation range between the lowest and highest points of a
region and is an important parameter in landslide hazard mapping. Landslide susceptibility
increases as relief increases, but at different levels with different geology (Dai et al. 2001;
Zhu et al. 2014). Bedrock incision caused by rivers can generate sufficient relief to pre-
dispose slopes to catastrophic land mass failures (Korup et al. 2007). The steepening of
hillslopes by erosion leads to an increase in relief above a threshold value and landslides
follow. Relief was divided into five classes:\50, 50–100, 100–150, 150–200 and[200 m.
3.2.4 Distance to river
The Sera River is a key feature of the Sera River Basin. Previous studies show that
landslides tend to occur along the sides of valleys as a result of groundwater flowing
towards streams and rivers, which in turn affects undercutting processes (Korup et al. 2007;
Tang et al. 2011; Zaruba and Mencl 2014). In this study only first- and second-order rivers
were considered as low-order streams are less important in the landslide process (Fan et al.
2013; Othman and Gloaguen 2013). Distance to river was calculated in ArcGIS and
categorised into five classes: \250, 250–500, 500–1000, 1000–2000 and [2000 m.
3.2.5 Topographic wetness index (TWI)
Topographic indices, such as TWI, are used to describe soil moisture distribution and
groundwater flow (Beven and Kirkby 1979; Moore et al. 1991). Therefore, it is important
to account for TWI when considering landslide processes (Pourghasemi et al. 2012;
Timilsina et al. 2014). TWI is defined as:
TWI ¼ln As
tan b
 ð1Þ
where A
s
is the specific catchment area (the local upslope area draining through a certain
point per unit contour length) and bis the slope. TWI is usually higher in flat, converging
terrain and lower in regions of steep, diverging land (Timilsina et al. 2014). TWI values
were normalised for a scale of 1–10 (1 the lowest, 10 the highest) and classified into six
classes: \2, 2–4, 4–5, 5–6, 6–8 and [8.
3.2.6 Lithology
Rock permeability and strength are characterised by geological parameters such as
lithology and structure. Therefore, determining rock boundaries and their overall distri-
bution is important when considering landslide processes (Ayalew and Yamagishi 2005).
Lithological data were grouped into five main domains (Table 1), and these were used for
analysis.
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4 Materials and methods
The ArcGIS software was used to prepare data and represent modelling results, while the R
statistical programme (R Development Core Team 2015) enabled logistic regression
analysis (modelling and validation processes of the study area). The flowchart showing the
different steps of the study is illustrated in Fig. 6.
4.1 Sample selection
Instead of using a single point to represent one landslide, this study considered the whole
area of moved landslide masses from the previous landslides. This is because single points
tend to not fully represent the characteristics of the landslide area (Timilsina et al. 2014).
Previous studies also show that sampling patterns are best when ‘‘seed cells’’ or gridded
points extracted from the landslide area are used to obtain attributes for landslide and
random landslide-free points (Brenning et al. 2005; Meusburger and Alewell 2009; Van
Den Eeckhaut et al. 2010). However, given the limited data considered in this study, the
small sample may not fully cover the diversity of all the factors in the study area, on which
the resulting model is dependent (Heckmann et al. 2014).
Addressing the issue of ‘‘rare events’ when applying logistic regression, King and Zeng
(2001) state that the number of non-events should typically be 2–5 times higher than that of
events for the model area (Heckmann et al. 2014). In addition, they propose endogenous
stratified sampling, that is, a sampling method that includes all events and a random sample
of non-events. The sample selection of this study followed the reasoning of King and Zeng
(2001): all the landslides events and a random sample of non-events around the current
landslide active area were chosen. As shown in Fig. 4, of the 24,768 gridded points in the
model area defined, 5992 were classified as landslide points (event points, 1) and 18,776
(non-event points, 0) as landslide-free points, giving a ratio of approximately 1:4.
4.2 The modelling strategy
Logistic regression allows the analysis of a problem where the result, measured with
dichotomous variables such as 1 and 0 (or TRUE and FALSE), is determined from one or
more independent factors (Menard 2002). Consideration of the influence of nindependent
variables (x
1
to x
n
) on the dependent variable (Y) generates the model statistics and
Table 1 Lithology domains and rock formations of the study region
Lithological
domain
Age Rock formation Description
1. Qal Quaternary Alluvium Gravel, sand, clay
2. Tb Pliocene Besirli Formation Sandstone, mudstone, conglomerate,
basalt, agglomerate
3. Tek Eocene Kabakoy
Formation
Andesite, basalt and their
pyroclastics, sandy limestone, tuff
4. KTb Palaeocene–Lower
Eocene
Bakirkoy
Formation
Sandstone, marl, shale, clayey
limestone, tuff
5. Pzm Palaeozoic Metamorphic
rocks
Gneiss, mica schist, chlorite schist
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coefficients of a formula used to predict the probability of a logit transformation of the
dependent variable. As such, logistic regression results do not define landslide suscepti-
bility directly. Instead, an inference can be drawn using the probability values. In the case
of landslide susceptibility mapping, logistic regression attempts to determine the model
that best describes the relationship between the presence or absence of the dependent
variable (landslides) and a set of independent variables. There are no universal criteria or
guidelines for the selection of independent variables, although there is a general agreement
that they should have a certain degree of affinity with the dependent variable, be fairly
represented across the study area, vary spatially within the study area and be measurable
(Ayalew and Yamagishi 2005). The independent variables employed in this study are
Fig. 6 Flowchart of the study
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slope, aspect, relief, lithology, distance to rivers and TWI, respectively. The independent
variables can be continuous or categorical, but the latter was preferred here. With regard to
the categorisation of the variables, the range of each class does not greatly affect the
landslide hazard prediction capacity (Remondo et al. 2003), so both expert-based and
landslide distribution-based classifications are applicable. Furthermore, due to the addition
of an appropriate function to the usual linear regression model, the variables may either be
continuous or discrete, or a combination of both types, and do not necessarily have normal
distribution (Lee and Sambath 2006).
Logistic regression coefficients can be used in the model to estimate the effect of each
of the independent variables on landslide occurrence (Pradhan and Lee 2010; Ayalew and
Yamagishi 2005). While regression coefficients are not readily interpretable, standardised
coefficients can be used to assess the relative importance of predictors (Ma
˘rga
˘rint et al.
2013). In addition, the data firstly need to be normalised in order to generate an accurate
model; combining data with different measuring scales can lead to problems in the
interpretation of final results. The logistic regression model allows the integration of both
continuous and discrete independent variables. In this study, all continuous variables were
converted to discrete variables according to their classes. Categorical variables can be
integrated into two ways: (1) by expressing the classes of each discrete parameter as
dummy variables (Guzzetti et al. 1999; Dai and Lee 2002; Nefeslioglu et al. 2008) or (2) by
computing landslide densities for discrete parameters and using these as the density pre-
dictors (Zhu and Huang 2006; Yilmaz 2009). This study used the latter approach to prevent
the creation of an excessively high number of dummy variables. Use of landslide densities
also allows for the representation of independent parameters on the same scale (Ayalew
and Yamagishi 2005). Landslide densities for slope, aspect, relief, curvature, lithology,
distance to rivers and TWI were computed using the following formulae (Bai et al. 2010):
LDi¼LAi=Ai
ðÞ
LA=AðÞ ð2Þ
where LD
i
is the landslide density value for class i,LA
i
and A
i
are the landslide area in
class iand the total area of class i, respectively, and LA and Aare the total landslide area in
the model region and the total area of the model region, respectively. A class with a high
landslide density corresponds with a parameter having a higher coefficient in the logit
function and hence is considered to play a greater role in landslide activity.
Therefore, the logit function that defines the probability of a landslide occurring (P)is
expressed as:
logit ¼ln P
1P

¼b0þb1x1þb2x2þ...þbnxnð3Þ
and hence,
P¼1
1þeb0þb1x1þb2x2þ...þbnxn
ðÞ ð4Þ
where b0...bnare constants.
The regression coefficients are computed using the maximum likelihood estimation
(Suzen and Doyuran 2004). Compared with linear regression, there is no unique solution
for logistic regression coefficients, hence why the maximum likelihood estimation follows
an iterative algorithm.
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For the susceptibility model, 80 % of the landslide and non-landslide points were used
as training samples. The remaining 20 %, randomly selected, were used as an independent
data set for validation and for testing the predictive potential of the logistic regression
model. Continuous susceptibility values, obtained from the model and ranging from 0 to 1,
were classified into five classes. Several approaches can be taken in this regard including
equal intervals, standard deviation-based separations, natural breaks method and quantiles.
There is, however, no agreement on the best method. Ayalew and Yamagishi (2005) state
that the use of equal intervals tends to emphasise one class over the other and recommend
the standard deviation approach as the best choice for class separation. Conversely,
Ma
˘rga
˘rint et al. (2013) recommend the natural breaks algorithm (Jenks 1977) which groups
similar values together thus maximising the differences between classes. This method was
applied here and five landslide susceptibility classes were chosen, namely very low, low,
medium, high and very high.
4.3 Evaluation
The goodness of fit for the susceptibility model was tested with the pseudo-R
2
, the
Nagelkerke
R2, the Brier score and the area under the curve (AUC) of the receiver
operating characteristic (ROC).
The Nagelkerke
R2can be interpreted as the proportion of explained variation in the
regression model. Therefore, it can be used as a measure of success with regard to pre-
dicting the dependent variable from the independent variables (Nagelkerke 1991).
Nagelkerke
R2is defined as:
R2¼R2
max R2
ðÞ ð5Þ
where
R2¼1L0ðÞ=L^
b
no
2=
nð6Þ
and
max R2

¼1L0ðÞ
2=
nð7Þ
where L^
b
and L(0) represent the fitted models with the independent variables and the
‘null’’ model fitted with only the intercept, respectively.
The Brier score provides a means of assessing relative accuracy and generates the ‘‘error
rate’ of the logistic regression model (Brier 1950). The formulation of the Brier score is as
follows:
BS ¼1
NX
N
t¼1
ftot
ðÞ
2ð8Þ
where f
t
is the probability forecasted in Eq. 3,o
t
is the observed outcome of the event at
instance tand Nis the number of forecasting instances. The Brier score ranges from 0 to 1
and measures the mean squared difference between the predicted probability and the
observed outcome. Therefore, a completely accurate forecast would generate a value of 0.
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Wald statistics, which evaluate the statistical significance of each coefficient b
j
in the
model, were calculated as follows:
Wj¼bj
SEbj

2
ð9Þ
where W
j
represents the Wald test and SEbjrepresents the standard error of coefficient b
j
for independent variable j.
Classification accuracy tables and the ROC methodology were used as validation for
both the training and test samples. The ROC curve is a useful method for representing the
quality of deterministic and probabilistic detections and forecast systems (Swets 1988). It
analyses the relationship between sensitivity and specificity of a binary classifier, in this
instance the occurrence of landslides. Sensitivity refers to the proportion of positives
correctly classified, that is, the proportion of landslides correctly identified when com-
paring predicted probability to observed values. Specificity measures the proportion of
negatives correctly classified, that is, the proportion of landslide-free regions correctly
identified (Flach 2010; Yesilnacar and Topal 2005). Conventionally, the curve is a plot of
the probability of a correctly predicted response to an event (true positive rate or sensi-
tivity) versus the probability of a falsely predicted response to an event (false positive rate
or 1-specificity), as the cut-off probability varies and in other words correctly predicting a
landslide at a certain location, and incorrectly predicting a landslide at a certain location.
The AUC evaluates the quality of the forecast system by describing the ability of the
system to anticipate correctly the occurrence or non-occurrence of pre-defined ‘‘events’’, in
this case landslides. AUC is measured on a scale of 0 and 1. When AUC =1, every
positive has scored higher than every negative and the forecast is completely accurate—the
model is ideal. Excellent models have AUC values greater than 0.9 and good models AUC
values greater than 0.7 (Ma
˘rga
˘rint et al. 2013).
5 Analysis and results
5.1 Landslide susceptibility model
Landslide densities were computed for each class of landslide-conditioning parameters
(Table 2), and these values were used in the logistic regression analysis. With regard to
slope angle, the highest landslide density values corresponded with angles of more than
40°. Higher landslide density values were also associated with eastern and western slopes,
concave curvature, higher TWI values (2–8), areas within 500 m of rivers and, in terms of
lithology, KTb and Tb.
The logistic regression coefficients and standardised coefficients obtained are given in
Table 3. The slope aspect in the ‘‘West’’ class has the highest coefficient, indicating its
strong influence, followed by distance to rivers (under 500 m) and slope angles (greater
than 40°). With regard to slope aspect, the ‘‘north’’ class produced the second highest
coefficient. This indicates that landslides in the Sera River Basin are first and foremost
related to slope characteristics and proximity to rivers, then lithology.
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5.2 Landslide susceptibility mapping
After the logistic regression modelling, the susceptibility values were classified from very
low to very high according to the natural breaks method algorithm. The upper threshold
values are presented in Table 4. This table shows that the very low and low susceptibility
classes account for 43.5 and 17.7 % of the study area. Conversely, about 27 % of the study
area is highly/very highly susceptible. Figure 7shows the classified landslide susceptibility
map. The map shows the area of the basin consisting of Tb, Tek and Pzm deposits and that
steep slopes are the most hazardous. It is observed that the banks of the Sera River and its
tributaries are the most susceptible to landslides. Similar results are observed for the area
surrounding the basin.
5.3 Evaluation
Previous studies include statements about evaluating the performance of logistic regression
models: (1) the significant Wald statistic for independent variables should be less than 0.05
(Bai et al. 2010; Dahal et al. 2012); (2) the Nagelkerke
R2should be greater than 0.2 (Clark
and Hosking 1986; Ayalew and Yamagishi 2005); (3) AUC should be greater than 0.7
(Hosmer and Lemeshow 2000; Song et al. 2008); and (4) the Brier score should be less
than 0.25 (Steyerberg et al. 2010). Based on these criteria, the regression model generated
in this study showing
R2of 0.545, Brier score of 0.133 and Wald statistics of \0.05 for
most classes of predictors is considered satisfactory (Table 3). The percentages of correctly
Table 2 Landslide density (LD) for the classes of the landslide triggering parameters
Slope (°) LD Aspect LD Curvature LD
\10 0.253 Flat 0.001 Concave 1.493
10–20 1.168 North 0.661 Flat 0.078
20–30 0.974 East 1.043 Convex 0.747
30–40 0.920 South 0.136
[40 4.057 West 2.181
Relief (m) LD Lithology LD Distance to river (m) LD
\50 0.058 Pzm 0 \250 1.359
50–100 0.268 KTb 1.075832 250–500 1.814
100–150 0.223 Qal 0 500–1000 0.036
150–200 0.211 Tb 1.385608 1000–2000 0
[200 0.930 Tek 0 [2000 0
TWI LD
\2 0
2–4 0.485
4–5 0.905
5–6 1.280
6–8 1.869
[8 0
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Table 3 Performance results and logistic regression coefficients of the model
Regression coefficient Standardised coefficient SE Wald v
2
(z)Pr([|z|)
(Intercept) -2.724 -6.622 0.096 -28.358 \0.001
Predictors
Slope (°)
\10 -2.362 -6.622 0.070 -33.966 \0.001
10–20 -1.394 -1.647 0.060 -23.221 \0.001
20–30 0.076 0.211 0.052 1.459 0.145
30–40 -1.441 -3.617 0.042 -34.443 \0.001
[40 2.362 5.927 0.070 33.966 \0.001
Aspect
Flat -7.685 -15.530 36.087 -0.213 0.831
North 2.498 4.674 0.110 22.631 \0.001
East -0.021 -0.0484 0.098 -0.217 0.828
South -1.986 -8.431 0.098 -20.330 \0.001
West 7.663 32.617 0.087 0.213 0.831
Curvature
Concave 1.366 2.555 0.016 87.026 \0.001
Flat -0.294 -0.667 0.043 -6.879 \0.001
Convex -1.366 -3.100 0.016 -87.026 \0.001
Relief
\50 0.830 1.554 0.111 7.511 \0.001
50–100 -2.278 -5.172 0.104 -21.933 \0.001
100–150 -0.880 -3.734 0.099 -8.898 \0.001
150–200 -1.580 -4.431 0.100 -15.801 \0.001
[200 -0.830 -2.328 0.111 -7.511 \0.001
Geology
Pzm NA NA NA NA NA
KTb -1.184 -1.399 0.024 -50.143 \0.001
Qal -8.188 -22.774 0.215 -38.048 \0.001
Tb 1.184 3.293 0.024 50.143 \0.001
Tek NA NA NA NA NA
Distance to river
\250 3.757 9.428 0.069 54.213 \0.001
250–500 5.503 11.120 0.067 82.321 \0.001
500–1000 -3.757 -7.592 0.069 -54.213 \0.001
1000–2000 NA NA NA NA NA
[2000 NA NA NA NA NA
TWI
\2NA NA NANANA
2–4 -1.093 -3.065 0.048 -22.756 \0.001
4–5 -0.353 -0.417 0.018 -19.656 \0.001
5–6 0.215 0.599 0.021 10.111 \0.001
6–8 0.628 1.5764 0.062 10.102 \0.001
[8-11.235 -22.705 1054.439 -0.011 0.991
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classified points were achieved for a cut-off value of 0.5 for the training and validation
samples. Table 5(classification accuracy results for the validation of the model) shows a
good, stable logistic regression model with an overall accuracy of 80.1 and 81.9 % for the
training and validation samples. Area under the ROC curves (Fig. 8) indicate that the
logistic regression model is highly accurate, for both the training and validation samples
generated high AUC values (89.3 and 83.0 %, respectively).
Therefore, the computed logistic regression model is representative of landslide activity
in the model area. The model can be used more widely to evaluate landslide susceptibility
in the Sera River Basin as the basin has similar geomorphological and geological char-
acteristics to the area under study.
6 Discussion
Areas with high susceptibility of landslides are found in close proximity to water courses.
Toe erosion and steep slopes near river banks lead to slope instability and thus to landslides
(Liu et al. 2004). Toe erosion, a main cause of landslides in river valleys refers to stream
flows that undercut banks, resulting in sloughing and prompting slope failures (Tamrakar
et al. 2014; Midgley et al. 2012). The Black Sea Region, including the Sera River Basin, is
dominated by broadleaf forests. The root systems of broad-leaf trees are extensive, a factor
that helps prevent landslides. However, along the rivers are settlements and arable land
(hazelnut farms) (Demir et al. 2015b), which has led to an increase in landslide occur-
rences, particularly where land has been cleared on the steep banks (Fig. 7) and roots and
other physical barriers have been removed (Pandey et al. 2007).
The steep slopes of the Sera River Basin may also be a factor in causing landslides.
Highly susceptible regions in this regard are mostly south of Lake Sera, where strata dip
uniformly in one direction and cause differential erosion. Similarly, the reason why the
western and northern banks of the Sera River are more susceptible to landslides as com-
pared to the eastern and southern banks may be due to the litho-structural characteristics of
the region. Slope influences the susceptibility of an area to landslides as well as the
magnitude of landslides (Grelle et al. 2011). The logistic regression model employed in
this study emphasised the importance of aspect and lithology, which leads to the con-
clusion that slope is a major factor in determining landslide activity in the Sera River
Basin. Rock lithologies such as sandstone, mudstone and claystone that have low shear
strength properties and thin beds are the most landslide prone in the Black Sea region
(Duman et al. 2005). In cataclinical slope areas where strata dip towards slope angle, such
as on the western and southern banks of the Sera River, slope instability and translational
slope failures and slumps are more likely (Liu et al. 2004; Grelle et al. 2011). Conversely,
Table 4 Upper threshold values
(TV), derived by Jenks’ method,
and percentages (%) of landslide
susceptibility classes from the
total area of the study region
Susceptibility class TV %
Very low 0.093 43.5
Low 0.280 17.7
Medium 0.491 11.5
High 0.709 13.4
Very high 0.999 13.9
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when the dip of the bed goes against the slope angle (anaclinal slope areas), strata tend to
be more resistant to erosion and landslides.
Despite the positive results obtained, the landslide susceptibility model presented in this
study, as is the case of any statistical method, has some inherent limitations. These are: (1)
all landslides regardless of type were considered which resulted in a high degree of
generalisation; (2) being small scaled, the model does not consider the large spatial
variability of local conditions (especially geotechnical ones) which influence landslide
occurrence; (3) the model assumes that landslides will occur under the influence of the
Fig. 7 Landslide susceptibility map of the Sera River Basin
Table 5 Percentages of correctly classified points with respect to training and validation samples, using a
cut-off value of 0.5
Landslide-free points Landslide points Overall accuracy
Training sample 85.6 60.0 80.1
Validation sample 96.0 67.2 81.9
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same combination of predictors. For a more complete model, variables such as the dip of
the bed/strata and vegetation cover could be included. The inclusion of more variables
would not necessarily mean the model performs better, as this depends on the quality of
data (Coe et al. 2000; Fabbri et al. 2003), but it would allow for testing and confirmation of
how vegetation cover and dip of the bed relate to landslide activity. In this regard, lack of
landslide records is a limiting factor. Despite the satisfactory results generated by the
logistic regression model, the sample size was limited and concentrated on one area of the
basin only. The expansion of the study area may allow for the inclusion of more landslide
data from the surrounding areas, thus producing a richer landslide inventory that may be
used for validation purposes. In addition, new methods are being developed for the
preparation of landslide inventory maps, e.g. Santangelo et al. (2015) present a semi-
automatic procedure using GIS for the digitalization of landslide obtained from aerial
photographs, reducing the subjectivity from manual visual transfer to the digital database.
Such methods may be used to improve the quality of the landslide inventory, although
high-resolution aerial photographs of the study area, which are currently unavailable, are
required for this purpose.
Landslide susceptibility values are not absolute but are relative (Fell et al. 2008).
However, even with limited data, the landslide susceptibility map is significant to policy-
makers as it allows the understanding of the increasing risk of landslide in the region. This
can help to prioritise funding for landslide risk mitigation measures at the municipal levels,
which is a preliminary stage for regional planning, and designing landslide risk mitigation
plans (Pellicani et al. 2014).
7 Conclusion
This research attempted to follow different steps in order to produce the landslide sus-
ceptibility using logistic regression model in a GIS environment. This helps to understand
the future landslide probability of the area and its spatial distribution, which is important in
terms of infrastructure development and land use management.
Fig. 8 ROC curves with
associated AUC values computed
from the training sample and the
validation sample
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Landslides are widespread geohazards caused by geology, geomorphology, hydrology,
climate, land use and other factors. Most of the Trabzon province, in the Eastern Black Sea
Region of Turkey, is currently at risk from landslides, mostly as a result of poor land use
practice and deforestation. This includes the Sera River Basin. The Sera Lake, located in
the north of the basin, was formed as a result of a landslide in 1950, and since then there
have been further landslides in the region. Therefore, it is important to assess landslide
susceptibility in the area. Hazard mapping allows us to understand past and present
landslide activity and thus to determine the future risks. Landslide susceptibility refers to
the spatial occurrence probability of landslides and can be modelled to a relatively high
degree of accuracy by using a combination of statistical approaches and GIS.
Several approaches exist for modelling landslide susceptibility. A logistic regression
model was employed in this study. The logistic regression model generated different
probabilities for the occurrence of landslides by considering and assessing the conditions
that led to past and present slope failures and landslides. The parameters selected included
slope angle, aspect, lithology, TWI, proximity to rivers and relative relief. The model
satisfied the criteria set for evaluating its performance and was therefore deemed to rep-
resent accurately the relationships between the selected parameters and potential landslide
activity in the Sera River Basin. The landslide susceptibility maps showed that regions
along streams and south of the Sera Lake are highly vulnerable to landslides. This is
attributed to ‘‘soft’ lithologies, which have low resistance to erosion and landslide pro-
cesses, and slope instability as a result of toe erosion, which is shaped by the distance to
water courses.
Other important causative factors of landslides include the dip of the bed, land use, and
the type and extent of vegetation cover. However, these factors were not considered. Field
investigations and the production of detailed geological maps (including strata dip angle
and directions) were also beyond the scope of this study. The incomplete landslide
inventory map of the region poses a problem for landslide susceptibility mapping as the
current landslide activity is concentrated north of the lake, and the map reflects this. A
means to improve model performance could be to expand the study area and consider a
larger scale. However, this would require detailed landslide records, which are currently
not available. Nevertheless, given the positive results obtained in this study, it can be
concluded that logistic regression could be a significant means by which the landslide
susceptibility in the Black Sea region can be assessed.
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... LSM is an essential tool that incorporates potential landslide locations (Senouci et al., 2021). The probability of a landslide occurring in a particular region owing to the effects of several causative factors is referred to as landslide susceptibility (Reichenbach et al., 2018). LSM is an essential step towards landslide risk management and helps in effective mapping of the spatial distribution of probable landslide manifestations (Dai et al., 2002). ...
... In the past, researchers have used a range of models to assess landslide susceptibility using technologies such as Earth observation (EO) and a geographic information system (GIS). The recognition and analysis of slope movements have been going on since the early 1970s (Brabb et al., 1972) and is still one of the most important components in performing LSM (Ercanoglu and Gokceoglu, 2002;Chacón et al., 2006;Guzzetti et al., 2006;Castellanos Abella and Van Westen, 2008;Floris et al., 2011;Catani et al., 2013;Pham et al., 2015;Reichenbach et al., 2018;Youssef and Pourghasemi, 2021;Liu et al., 2021). ...
... Presently, two approaches -(1) statistical and (2) machine learning -are practised for LSM for investigating the landslide predisposing factors and for mapping the geographical distribution of landslide processes. Reichenbach et al. (2018) classified landslide susceptibility models into six main groups: (1) classical statistics, (2) index-based, (3) machine learning, (4) multi-criteria analysis, (5) neural networks, and (6) others. Research by Reichenbach et al. (2018) also depicted that before 1995, only 5 models were used for LSM, but in recent times, an investigation of 19 other models was carried out, which yielded good results. ...
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In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) for LSM in the province of Belluno (region of Veneto, northeastern Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least “important” features by using a common threshold of 0.30 for statistical and 0.03 for ML algorithms. Conclusively, we found that removing the least important features does not impact the overall accuracy of LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least important ones, namely the aspect plan and profile curvature, topographic wetness index (TWI), topographic roughness index (TRI), and normalized difference vegetation index (NDVI) in the case of the statistical model and the plan and profile curvature, TWI, and topographic position index (TPI) for ML algorithms. This confirms that the requirement for the important conditioning factor maps can be assessed based on the physiography of the region.
... Relief amplitude refers to the difference between the altitude of the highest point and the altitude of the lowest point in a particular area and can effectively reflect the gravitational potential energy of terrain that is closely related to landslide occurrence [39]. In this study, the relief amplitude was reclassified into five classes: Gully density, defined as the channel length per unit area, represents an effective factor in LSM [1]. ...
... The SPI reflects the intensity of stream erosion on the surface and therefore influences the occurrence of landslides [39]. The SPI values were divided into six classes: <6, 6-12, 12-18, 18-24, 24-30, and >30 ( Figure 2(g)). ...
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In the present study, a hybrid machine learning model was designed by integrating ant colony optimization (ACO), particle swarm optimization (PSO), and support vector machine (SVM) algorithms. The model was used to map the landslide susceptibility of the Anninghe Fault Zone in Sichuan Province, China. Based on this, 12 conditioning factors associated with landslides were considered, namely altitude, slope angle, cutting depth, slope aspect, relief amplitude, stream power index (SPI), gully density, lithology, rainfall, road density, distance to fault, and peak ground acceleration (PGA). The overall performance of the two resulting models was tested using the receiver operating characteristic (ROC), area under the ROC curve (AUC), Cohen’s kappa coefficient, and five statistical evaluation measures. The success rates of the ACO-PSO-SVM model and the SVM model were 0.898 and 0.814, respectively, while the prediction rates of the two models were 0.887 and 0.804, respectively. The results show that the ACO-PSO-SVM model yields better overall performance and accurate results than the SVM model. Therefore, in conclusion, the ACO-PSO-SVM model can be applied as a new promising method for landslide susceptibility mapping in subsequent studies. The results of this study will be useful for land-use planning, hazard prevention, and risk management.
... Globally, rock landslides cause approximately 300 deaths per year and a property damage of about 1 billion dollars (Zhan et al. 2019;Liao et al. 2019). According to Raja et al. (2017) and Zhang et al. (2020a, b, c, d), rock landslides account for 3% of all deaths caused by natural disasters. Hydraulic fracturing (HF) is a physical phenomenon of the initiation, propagation and penetration of micro fracture in rock under high water pressure until generate macro fracture and causes slope failure (Zhan and Cen 2007;Wang et al. 2020;Lyu et al. 2020). ...
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Hydraulic fracturing (HF) is an important reason for inducing rock landslides. The impacts of the angle between the transverse pressure stress and the initial fracture, the transverse pressure stress, the length and aperture of the initial fracture on the critical water pressure stress of HF were analyzed based on the model tests of cement mortar specimens. The HF numerical simulations were carried out and the fracture aperture, pore pressure, strain and stiffness degradation index were analyzed, and the HF mechanism of rock was revealed. Taking a rock slope along G205 in China as an example, the landslide stability under high groundwater pressure was studied by numerical simulations, and the whole processes of the expansion, propagation, penetration and the rock slope failure were reproduced. The results showed that when HF occurred on the cement mortar specimens, a large amount of water seeped from the fracture and made a dull sound of failure, the water pressure on the fracture plane decreased rapidly, but not completely penetrated, and there was still a certain residual strength of the specimen. The HF of rock is a quasi-brittle failure, including static stage, micro fracture propagation stage and macro fracture formation stage. There are 3 dangerous rocks on the rock slope, among which WYT3 is in a stable state under the natural condition and in an unstable state under the rainstorm condition and seismic condition. The fracture propagation of WYT3 under the action of high groundwater pressure experienced slow development stage, rapid development stage and penetration stage, among which the slow development stage lasted the longest, and the slope failure occurred immediately after the fracture penetrated.
... have achieved higher AUC scores at 0.93, and 0.977(Micheletti et al. 2014;Song et al. 2018). The performance of the models in this case study is likely limited by the data, as ML model performance is limited by the quality and quantity of data supplied(Fabbri et al. 2003;Raja et al. 2017).The 19 input features were shown to have varying levels of importance in modelling landslide potential dependent on the regions used in each training-testing permutation. The in uence that each feature has on modelling landslide potential is location speci c, as the spatial distribution of certain geohazards varies. ...
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Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur on the seafloor to support planning and development projects. Using the Gulf of Mexico (GoM) as a case study, this paper performs Landside Susceptibility Mapping (LSM) using a Gradient Boosted Decision Tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the U.S. Exclusive Economic Zone (EEZ) where water depths are greater than 120 meters. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting where the potential source location of sediment instability is more likely to occur. Results of a permutation modelling approach indicate that LSM accuracy is sensitive to training set size with accuracies becoming more stable as the number of observations increases. The influence that each input feature has on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.
... In an area, the vicinity to a water body increases the probability of a slope failure by raising fluid pore pressure and slope toe erosion, that's why distance to drainage (Fig. 6a) is determined as a key influencing parameter in the slope failure [34,90]. The project area comprises a large drainage network because of the mountainous terrain which was extracted by using ALOPS PALAR DEM and further reclassify in Arc GIS 10.8. ...
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The Himalayan region, a rugged mountain zone is among the most susceptible zones to the landslide hazard due to its terrain, geography, and active tectonics. Machine learning (ML) techniques are most advanced and precise methods to develop landslide susceptibility model (LSM). The current study was designed to analyze and assess the landslide susceptibility using ML approaches for District Hattian Bala, NW Himalayas, Pakistan. The historical satellite imageries are used to generate spatiotemporal landslide inventories of year 2005, 2007 and 2012. A spatial database was created pertaining to topographic, environmental, geologic, and anthropogenic factors including slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI) and land use/ land cover (LULC). These LCFs were selected to analyze periodic landslide susceptibility in the region. The experimental design utilized 349, 393, and 735 landslide inventory of 2005, 2007, and 2012 respectively. Two ML models, i.e., Random Forest (RF) and Logistic Regression (LR) were applied to assess landslide susceptibility determine by thirteen landslide causative factors (LCFs). The spatiotemporal landslide inventory was partitioned into training (70%) and testing (30%) landslides for respective years to check the prediction accuracies of selected ML models. Comparative analysis of different LSMs was performed by the Receiver Operator Curves – Area Under Curves (ROC-AUC). The resultant accuracy, MAE, RMSE, Kappa, Precision, Recall, F1 indicated that RF outperformed the LR model. The study aims to minimize losses to lives and potential economic damage linked with recurrent slope instabilities in the region. It is anticipated that use of ML algorithms would support concerned authorities and organizations to effectively plan and manage landslide hazard in the region.
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Landslide disasters frequently occur along the highway G30 in the Guozigou Valley, the corridor of energy, material, economic and cultural exchange, etc., between Yili and other cities of China and Central Asia. However, little attention has been paid to assess the detailed landslide susceptibility of the strategically important highway, especially with high spatial resolution data and the generative presence-only MaxEnt model. Landslide susceptibility assessment (LSA) is a first and vital step for preventing and mitigating landslide hazards. The goal of the current study was to perform LSA for the landslide-prone highway G30 in Guozigou Valley, China with the aid of GIS tools and Chinese high resolution Gaofen-1 (GF-1) satellite data, and analyze and compare the performance of the maximum entropy (MaxEnt) model and logistic regression (LR). Thirty five landslides were determined in the study region, using GF-1 satellite data, official data, and field surveys. Seven landslide conditioning factors, including altitude, slope, aspect, gully density, lithology, faults density, and NDVI, were used to investigate their existing spatial relationships with landslide occurrences. The LR and MaxEnt model performance were assessed by the receiver operating characteristic curve, presenting areas under the curve equal to 0.85 and 0.94, respectively. The performance of the MaxEnt model was slightly better than that of the LR model. A landslide susceptibility map was created through reclassifying the landslides occurrence probability with the classification method of natural breaks. According to the MaxEnt model results, 3.29% and 3.82% of the study region is highly and very highly susceptible to future landslide events, respectively, with the highest landslide susceptibility along the highway. The generated landslide susceptibility map could help government agencies and decision-makers to make wise decisions for preventing or mitigating landslide hazards along the highway and design schemes of highway engineering and maintenance in Guozigou Valley, the mountainous areas.
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Landslides caused countless economic and casualty losses in China, especially in mountainous and hilly areas. Landslide susceptibility mapping is an important approach and tool for landslide disaster prevention and control. This study presents a landslide susceptibility assessment using frequency ratio (FR) and index of entropy (IOE) models within a geographical information system for She County in the mountainous region of South Anhui, China. First, the landslide locations were ascertained in the study area using historical landslide records, aerial photographs, and multiple field surveys. In all, 502 landslides were identified and randomly divided into two groups as training (70%) and validation (30%) datasets. Additionally, the landslide-influencing factors, including slope angle, slope aspect, curvature, landform, lithology, distance to faults, distance to roads, distance to rivers, rainfall, and normalized difference vegetation index, were selected and their relative importance and weights were determined by FR and IOE models. The results show that the very high and high susceptibility classes cover nearly 50% of the study area. Finally, the comprehensive performance of the two models was validated and compared using receiver operating characteristic curves. The results demonstrated that the IOE model with the area under the curve (AUC) of 0.802, which is slightly better in prediction than the FR model (AUC = 0.786). The interpretation of the susceptibility map indicated that landform, slope degree, and distance to rivers plays a major role in landslide occurrence and distribution. The research results can be used for preliminary land use planning and hazard mitigation purposes.
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Landslide dams are a common type of river disturbance in tectonically active mountain belts with narrow and steep valleys. Here we present an unprecedented inventory of 828 landslide dams triggered by the 2008 Wenchuan earthquake, China. Of the 828 landslide dams, 501 completely dammed the rivers, while the others only caused partial damming. The spatial distribution of landslide dams was similar to that of the total landslide distribution, with landslide dams being most abundant in the steep watersheds of the hanging wall of the Yingxiu-Beichuan Thrust Fault, and in the northeastern part of the strike-slip fault near Qingchuan. We analyzed the relation between landslide dam distribution and a series of seismic, topographic, geological, and hydrological factors.
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Landslide inventory maps (LIMs) show where landslides have occurred in an area, and provide information useful to different types of landslide studies, including susceptibility and hazard modelling and validation, risk assessment, erosion analyses, and to evaluate relationships between landslides and geological settings. Despite recent technological advancements, visual interpretation of aerial photographs (API) remains the most common method to prepare LIMs. In this work, we present a new semi-automatic procedure that makes use of GIS technology for the digitization of landslide data obtained through API. To test the procedure, and to compare it to a consolidated landslide mapping method, we prepared two LIMs starting from the same set of landslide API data, which were digitized (a) manually adopting a consolidated visual transfer method, and (b) adopting our new semi-automatic procedure. Results indicate that the new semi-automatic procedure (a) increases the interpreter’s overall efficiency by a factor of 2, (b) reduces significantly the subjectivity introduced by the visual (manual) transfer of the landslide information to the digital database, resulting in more accurate LIMs. With the new procedure, the landslide positional error decreases with increasing landslide size, following a power-law. We expect that our work will help adopt standards for transferring landslide information from the aerial photographs to a digital landslide map, contributing to the production of accurate landslide maps.
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