geosciences
Article
A Hybrid Spatial Multi-Criteria Evaluation Method
for Mapping Landslide Susceptible Areas in Kullu
Valley, Himalayas
Sansar Raj Meena 1 , Brijendra Kumar Mishra 2
1
2
*
and Sepideh Tavakkoli Piralilou 1, *
Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria;
sansarraj.meena@sbg.ac.at
Department of Geology, University of Delhi, New Delhi-06, Delhi 110007, India; brijsmishra@gmail.com
Correspondence: sepideh.tavakkoli-piralilou@stud.sbg.ac.at
Received: 28 February 2019; Accepted: 1 April 2019; Published: 3 April 2019
Abstract: In this paper we report our results from analysing a hybrid spatial multi-criteria evaluation
(SMCE) method for generating landslide susceptibility mapping (LSM). This study is the first of
its kind in the Kullu valley, Himalayas. We used eight related geospatial conditioning factors from
three main groups: geological, morphological and topographical factors. Our landslide inventory
dataset has a total of 149 GPS points of landslide locations, collected based on a field survey in July
2018. The relationships between landslide locations and conditioning factors were determined using
the GIS-based statistical methods of frequency ratio (FR), multi-criteria decision-making (MCDM)
and the integration method of hybrid SMCE. We compared the performance of applied methods by
dividing the inventory into testing (70%) and validation (30%) datasets. The area under the curve
(AUC) was used to validate the results. The integration method of hybrid SMCE gave the highest
accuracy rate (0.910) compared to the other two methods, with 0.797 and 0.907 accuracy rates for
the analytical hierarchy process (AHP) and FR, respectively. The applied methodologies are easily
transferable to other areas, and the resulting landslide susceptibility maps (LSMs) can be useful for
risk mitigation and development planning purposes in the Kullu valley, Himalayas.
Keywords: natural hazards; landslide susceptibility mapping (LSM); frequency ratio (FR);
multi-criteria decision-making (MCDM)
1. Introduction
Landslides are among the most damaging geological hazards in mountainous regions such as
the Himalayas. Globally, every year hundreds of people die as a result of landslides, which also
considerably affect local and global economies [1]. The Himalayan orogeny, which is tectonically
the most active mountainous region in the world, is highly vulnerable to landslides and associated
hazards. Landslide susceptibility mapping (LSM) is an effective tool for understanding the probability
of the spatial distribution of future landslides [2]. The LSM requires a multi-criteria decision-making
(MCDM) approach to generate maps with high levels of accuracy and reliability, which can then be
used as input for disaster management plans [3]. GIS-based MCDM is an important geospatial analysis
method which combines geospatial and non-spatial data to produce LSMs of an area [4]. The GIS tool
integrated with MCDM methods provides a geospatial framework to organise these various thematic
layers into a hierarchical structure and examine the relationships between the different geospatial
components [5]. Landslide conditioning factors have been analysed to map susceptible areas in several
mountainous regions around the world since the early 1980s [6,7]. However, nowadays with growing
geo-computation, there are new methods like automatic and semi-automatic computation for LSM and
Geosciences 2019, 9, 156; doi:10.3390/geosciences9040156
www.mdpi.com/journal/geosciences
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risk assessments [8,9]. The process of creating these LSMs involves several qualitative and quantitative
approaches and methods [10]. Attempts have been made to define landslide susceptibility classes
based on qualitative methods by overlaying geological and topographical slope attributes based on
landslide inventory maps [11]. There are a number of commonly used LSM methods involved, e.g.,
analytical hierarchy process (AHP) [12], analytical network process (ANP) [13,14], frequency ratio
(FR) [15–17], fuzzy logic (FL) [15,16] and artificial neural network (ANN) [17]. The AHP is one of
the GIS-based-MCDM methods that has been successfully applied by many scientists to produce
landslide susceptibility maps [6,18]. The most common qualitative methods for LSMs, like AHP,
use landslide inventories and geospatial parameters within a hieratical structure to recognise sites of
comparable geological and geomorphological characteristics, which are susceptible to slope failure.
However, weights of geospatial parameters are determined from experts’ knowledge on the subject
and area. Although the AHP is a well-known and popular method, it relies on a pairwise matrix
based on expert opinions, thus introducing a degree of subjectivity in assigning weightings to the
thematic layers for LSM [19]. In the case of applying the FR method, our landslide inventory dataset
was associated with each conditioning factor to indicate its importance [20]. FRs show the level
of correlation between the inventory dataset and the considered conditioning factors as input data
for susceptibility modelling and mapping [21,22]. The level of the correlation is derived from the
probability of an occurrence to the probability of a non-occurrence of landslides in LSM [23–25]. Hybrid
SMCE is a robust, GIS-based methodology used for solving geospatial problems for decision makers,
e.g., LSM [23,24]. These methods have provided acceptable results in accurately determining the
susceptible landslide zones [25]. The statistical methods analyse the link between controlling factors of
landslides and their distribution. The quantitative methods are mainly used to decrease bias in the
weight assessment process. Therefore, the objective of using these quantitative methods is to produce
more reliable susceptibility maps based on an integrating AHPs and FRs into a hybrid SMCE.
Landslide types mainly rockfalls, rockslides and debris flow, are the most common natural
hazards in the Kullu valley, which cause significant economic damage and are of great concern to
public administrators and geoscientists [26]. The Kullu valley in Himachal Himalayas has a known
history of large-scale landslides and different modes of slope deformation. There was a significant
landslide in Kullu valley in 1995 that resulted in the death of 65 people and immense devastation in the
Luggar Bhatti area of Kullu town itself—a very popular tourist destination. In this paper, we present
a synoptic assessment of landslide susceptibility assessments and GIS-based statistical methods in
a comparative study of the AHP, FR and hybrid SMCE methods for creating LSMs in the Kullu valley
along the Larji–Kullu tectonic window (LKTW) zone in the higher Himalayas.
The results of this study led to the preparation of landslide susceptibility maps for the Kullu
valley and to the identification of zones that are vulnerable to future slope deformations.
2. Study Area and Inventory Dataset
The Kullu valley is part of the Beas River basin. The River originates in the Pir-Panjal range,
near Rohtang crest (4038 m), and flows transversally to the two parallel ranges of Pir-Panjal and
Dhauladhar (Figure 1). This district name, Kullu, comes from the name ‘Kulata’, the first mention
of which was found on the coin of Raja Viryasasya of ‘Kulata’ dating back to the first or second
century [27]. According to ancient Hindu scriptures, the area was also known as ‘Kulantapitha’—the
end of the habitable world.
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Figure 1. Map showing the location of the study area, landslide inventory map with the distribution of
training and validation datasets, and field photographs: (1) debris slide, (2) debris slide, (3) rockfall,
and (4) debris slide.
The Kullu district is situated in the transitional zone between the lesser and greater Himalayan
Mountain ranges in the central part of Himachal Pradesh. It has rugged topography with altitudes
ranging from 1300 m to 6000 m above mean sea level. The higher reaches are endowed with
snow-covered peaks and glaciers. The Kullu district borders the Shimla district and part of Kinnaur
in the south-east, Lahaul and Spiti in the north-east, Kangra and parts of Chamba in the north-west,
and Mandi in the south-west. The district’s total area district is 5503 sq km., which is 9.88% of
Himachal’s total area. Sutlej and Beas are the main Rivers in the district. In general, Kullu gets cold
1
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temperatures and moderate precipitation, mostly during July, August, December and January. Kullu
valley hosts the main river of Beas basin, and its sub-basins Parvati, Hural and Sainj Rivers, which are
tributaries of the Beas River. The valley is known for a vibrant cultural heritage, attracting international
tourism, as well as hydroelectric construction activities with a series of hydroelectric projects (viz.
Parvati valley Hydel Projects and Sainj valley) and providing a corridor of strategic importance to
upper reaches of Himalayas.
The present study began with the creation of an inventory map of landslides in the Kullu valley
based on manual landslide detection from Rapid Eye satellite imagery with a spatial resolution of
5 m enhanced with the resulting landslide inventory based on extensive fieldwork. Furthermore,
eight GIS-based thematic layers of factors, which contribute to landslides, were analysed for LSM
using the three different methods of AHP, FR and hybrid SMCE. The thematic layers were lithology,
landforms, distance to faults line, distance to lineaments, elevation, slope, slope aspect, distance to
roads, and distance to drainage. Finally, the resulting susceptibility maps produced using these three
methods were compared and evaluated using validation datasets and the most influential causative
factors triggering landslides within the LKTW domain were discussed. The methods applied in this
study were dependent on different logical explanations to create a landslide susceptibility map of the
Kullu valley and also decrease the influence of the subjective evaluation of a subject specialist.
Landslide Inventory Dataset
The landslide inventory map illustrates the active landslide sites along with their properties such
as the type of landslide, structural attributes, and distance from the road. These slope deformation
features are related to morphological, geological and climatic conditions at the locations. Thus,
these attributes can predict future conditions, which could lead to landslide occurrences in the
area. The first step was to identify landslide locations in the satellite imagery and to evaluate
landslide-prone areas [28]. To this end, active landslide locations were mapped, and inventory
maps were prepared using different techniques, including satellite image interpretation, an extensive
field survey, and literature searches for historical landslide records [29,30]. The landslide inventory
map showed the spatial distribution of landslides in the study area. The landslide inventory dataset
was generated from an extensive field survey carried out in July 2018. A total of 149 landslide locations
were identified, and these were randomly divided into two groups with 70% (105) used for training the
methods and 30% (44) for validating the results. The Kullu valley exposes highly dissected topography,
it is susceptible to physical erosion and heavy rainfalls, and lies in the alpine climate zone, meaning
that new landslides are frequent. The drainage also produces flash floods during rainfalls, which cause
debris flow. We classified landslide types based on the classification method described in Reference [31].
In our study area, dominant debris slides along with rockfall were present in some areas. Examples of
landslide types are shown in Figure 1.
Our landslide inventory was separated into two datasets: one for training and the other for testing.
This is a very common approach that has been used in several natural hazard studies [32–34]. Training
and testing datasets are chosen based on the size of the study area, inventory data and the applied
methodology. Currently, there are no standard methodologies for the selection of testing and training
samples [32]. In Reference [33], the authors assign different ratios for various methods. The points
were sampled randomly from the body of landslides due to the complexity of forms, sizes and shapes
of landslide features.
3. Workflow
3.1. Conditioning Factors
For this study, we evaluated the ability to derive representative conditioning factors from the
resampled (30 m) advanced spaceborne thermal emission and reflection radiometer (ASTER) digital
elevation model (DEM) to simplify the data needed for landslide assessment. Figure 2 shows the (30 m)
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ASTER DEM-derived and field-based data layers representing the study area’s landslide conditioning
factors. A description of the conditioning factors is given below.
Terrain slope angle is one of the prominent reasons for slope failure [34]. The topographic slope
angle is widely used in landslide susceptibility analysis since landslides are directly linked to slope
angle, and it is accepted that terrains with a high slope angle are more susceptible to failure. The study
area’s slope map was divided into five slope categories from 0◦ to >40◦ by intervals of 10◦ .
The terrain slope aspect factor is also considered to be an essential factor in landslide susceptibility
analysis [35]. The terrain slope aspect factor affects landslides as it relates to meteorological criteria
such as precipitation direction and the average amount of sunshine. We classified Kullu valley’s terrain
slope aspect into ten classes: north (0◦ –22.5◦ ; 337.5◦ –360◦ ), northeast (22.5◦ –67.5◦ ), east (67.5◦ –112.5◦ ),
southeast (112.5◦ –157.5◦ ), south (157.5◦ –202.5◦ ), southwest (202.5◦ –247.5◦ ), west (247.5◦ –292.5◦ ),
northwest (292.5◦ –337.5◦ ), north (337.5◦ –360◦ ) and flat (0◦ ).
Elevation is another essential factor of LSM, as many geomorphological and geological processes
are controlled by this factor [36]. It is used to define the study area’s local elevation. The elevation
category refers to the elevation range between the lowest and highest points of a region [37]. To find
the number of landslides in different elevation classes, four altitude groups were considered in
classifying the terrain elevation: 0–1000 m, 1000–3000 m, 3000–4500 m and >4500 m above mean
sea level. However, landslides in the first class are dominant (43.54%) due to lithological and
geomorphological characteristics.
Drainage is another major controlling factor to be considered in landslide analysis. Drainage
provides water which causes material saturation, resulting in landslides in the valleys [38]. Therefore,
the effect of drainage and its distance to landslides plays a significant role in slope failures. The study
area was classified into five different buffer ranges. The buffers zones were constructed for intervals of
0–100 m, 100–200 m, 200–300 m and >300 m distance.
Distance to roads is a very prominent causal factor for landslide occurrence [24]. The study area
was divided into four different buffers zones, which designated the influence of Landslides caused by
roads. The interval of buffer zones was 0–50 m, 50–100 m, 100–150 m and >150 m.
Lithology is one of the most crucial factors in landslide studies, due to the fact that different
lithological units have different geological strength indices, permeability and susceptibility to
failure [39]. It is widely accepted that lithology is one of the most crucial landslide conditioning
factors [40,41]. We have thirteen lithological units in our study area. The lithological layer was
prepared based on quadrangle maps available from the Geological Survey of India (GSI) with a scale of
1:250,000. The aerial distributions analysis performed according to the lithological units showed that
most landslides were identified in areas of micaceous sandstone of the LKTW. The other lithological
units were pale to green quartzite, phyllite, schist, schistose quartzite, dolomite, purple limestone,
sandstone, Wangtoo granite and streaky banded gneisses.
Understating landform units is a very important in landslide studies. The landforms class can
explain highly dissected zones within the region, and landslide activity that is likely to occur. Kullu
valley’s landform resulted from GSI, and was classified into nine landform units: the active floodplain,
channel island, piedmont slope, river, glaciated terrain, snow cover, younger alluvial plain, highly
dissected terrain, moderately dissected terrain, and barely dissected terrain. The highly dissected,
moderately dissected and glaciated areas are prone to landslide activity.
In our study area, faults are the primary causative factor controlling landslides [41]. Faults create
a gap between two distinctive lithological units and generate fractures and joints within the lithological
unit that can propagate landslide activity [42]. Thus, distance to faults plays a crucial role in landslide
occurrence. Regions that are closer to faults were also more affected by several earthquakes that
occurred in this area. The faults were classified, and the buffer zones were generated and divided
into three different buffer ranges, based on the distance to faults, for intervals of 0–500 m, 500–1000 m,
1000–1500 m and >1500 m distance.
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Figure 2. Cont.
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Figure 2. Thematic maps used in this study (a) elevation, (b) slope angle, (c) slope aspect, (d) roads,
(e) drainage, (f) faults, (g) landforms, and (h) lithology. These landslide-conditioning factors were
derived from 30 m ASTER DEM and fieldwork carried out in the study area.
3.2. Landslide Susceptibility Mapping Using Different Methods
Landslide susceptibility analysis was carried out using the AHP, FR and hybrid SMCE
geo-statistical methods in Kullu valley, Himachal Himalayas.
3.2.1. The AHP Method
The AHP was developed in [43], and can be applied to weight-related factors of spatial problems
in GIS environments [44,45]. It is a common tool for analysing complicated spatial problems
focusing on site selection, urban planning, and natural hazard susceptibility analysis [46]. The AHP
is a decision-making process based on multi-criteria and multiple objectives, and involves the
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incorporation of expert knowledge [47]. A hierarchical order of factors and numerical values is
established based on the importance of each factor. Subsequently, these factors are integrated, and each
factor is weighted according to its importance [48]. In addition, the correlative pairwise comparison
matrix is established to utilise AHP. This matrix is constructed using values that represent experts’
judgments by comparing the importance of each factor in relation to all the other related factors [14].
Each layer is based on a nine-point rating scale and is included in the matrix as developed shown
in [49] (See Table 1). An expert specifies factor values. In this research, both determining decision
options and comparing the parameters were based on our landslide inventory dataset. Each factor
weight from the matrix class was multiplied by the weight class. Local representation of factors
determined the susceptibility map results. These representations can be based on different parameters,
including natural (lithology, distance to faults), human-made (roads and other engineering structures),
causal (slope, aspect, lithology) and triggering (precipitation, seismicity) [50]. All these factors were
weighted in the pairwise comparison matrices of the AHP based on expert knowledge. The principle
of transitivity is important in AHP for any given three factors (such as f1 , f2 and f3 ) and is defined
as follows: if f1 > f2 and f2 > f3 , then f1 > f3 . The principle of transitivity is a basis for conditioning
factors weighing in AHP. Due to this principle, a consistent pairwise comparison matrix would require
that if 2f1 > f2 (i.e., f1 is two times more preferable than f2 ) and 4f2 > f3 , then 8f1 > f3 to cover the
transitivity principle [5,51]. Therefore, it is necessary to compute the consistency of expert comparisons
in matrices in each stage [12]. Inconsistency can be defined based on the observation that λ_max > n
for comparison matrices and λ_max = n if C is a consistent comparison. The consistency ratio (CR)
can be defined by Equation (1):
CR = (λ_max − n)/(RI(n − 1))
(1)
where RI is the random index of a randomly created pairwise comparison matrix and for n = 2, 3,
4, 5, 6, 7, 8 and 9, RI = 0.00, 0.52, 0.89, 1.11, 1.25, 1.35, 1.40 and 1.45, respectively [52]. A consistency
ratio of <0.10 means an acceptable level of consistency, whereas a CR > 0.10 points to a degree of
inconsistency [43].
3.2.2. The FR Method
FR is a common geospatial assessment tool that provides the probabilities of distributing the
presence and absence of a spatial phenomenon for each conditioning factor [53]. Landslide conditioning
factors can merely be weighted by considering the ratio of observed landslides to the whole study area.
Since this method can find the correlation between the spatial phenomenon and factor classes, it is
a useful geospatial assessment tool for understanding the spatial relationship between landslides and
individual conditioning factors [54]. For computing the FR weights, the ratio of landslide inventory
points was identified for all classes within each factor considered in the current study. The dataset of
landslide inventory points was overlaid with the conditioning factors to obtain the area ratio for each
factor class to the total area. The FR weights are obtained by dividing the landslide occurrence ratio in
a class by the area ratio in that class [55].
A final susceptibility map can be produced using a linear combination of the sum of each factor’s
weights (see Equation (2)):
LSMFR = FRw1 + Frw2 + FRw3 + . . . + FRw9
(2)
where FRwi is the corresponding FR weight for the ith factor. FR weights indicate a higher correlation
of that class in triggering landslides.
3.2.3. Hybrid SMCE Method
The present hybrid SMCE method is an integrated method of a traditional SMCE with the
data-driven method of FR, which enable users to solve the spatial problems associated with natural
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hazard susceptibility mapping [20]. Alternatives of different factors are defined as lines, points, and
areas in this approach. Therefore, the resulting final maps are the results of landslide causal factors [23].
The hybrid SMCE approach incorporates spatial analysis and GIS to use both spatial and non-spatial
input data to produce the final maps [56]. In hybrid SMCE, input layers are spatially represented
as factors. Based on the criteria tree, input layers are grouped, weighted and normalised from their
original values to the 0–1 value range.
Table 1. Pairwise comparison point-based rating scale of AHP [49].
Importance
Definition
Explanation
1
3
5
7
Equal importance
Moderate importance
Strong importance
Very strong importance
9
Extreme importance
2, 4, 6, 8
Intermediate values
Contribution to objective is equal
The attribute is slightly favoured over another
The attribute is strongly favoured over another
The attribute is very strongly favoured over another
Evidence favouring one attribute is of the highest
possible order of affirmation
When compromise is needed
The output of hybrid SMCE is one or more composite index maps, which indicates the
extent to which criteria do or do not match in different areas and supports decision-making [57].
The multi-criteria evaluation of the AHP method is used as the theoretical background of the hybrid
SMCE method. The steps involved in the operation of hybrid SMCE are problem analysis, weighing
the factors, standardisation and finally generating the output map. The values in various input
maps have different meanings and are probably shown in different units of measurement, such as
percentages, meters, distance in meters, or land cover classes [58]. Finally, the landslide conditioning
factors were weighted using direct, pairwise, and rank ordering comparison (see Table 2), and the
output is a composite index map [59].
Therefore, in this study:
•
•
•
For the AHP model, we applied two levels of weightings for eight factors and classes. All weights
were generated from pairwise comparison matrices of AHP, which is a widely used method in
several natural hazard susceptibility modelling and mapping.
For the FR model, we used only one level of the weights resulting from the FR calculations for
each class, and the final landslide susceptibility map was produced from these weights.
For the hybrid SMCE, we had two different levels of weightings namely factors and classes. As it
is an integration methodology of AHP and FR, the resulting weights of AHP were used for the
conditioning factors. Furthermore, weightings of the second level hybrid SMCE were from FR.
Table 2. The frequency ratio (FR) values and AHP weights for each class.
Factors & AHP
Weights
Landforms
0.112
Classes
Active flood plain
Channel island
Glacial terrain
Highly dissected hill
and valley
Moderately dissected hill
and valley
Piedmont slope
River
Snow cover
Younger Alluvial Plain
Pixels of Each
Class
% of
Pixels
Landslide
Pixels
% of
Pixels
FR
AHP
Weights
1242
93
81,464
0.02
0
1.33
0
0
0
0
0
0
0
0
0
0.063
0.07
0.068
115,218
1.88
0
0
0
0.068
732,590
11.98
26,100
33.72
0.68
0.174
2,111,092
2501
3,027,191
44,690
34.52
0.04
49.5
0.73
0
0
49,500
1800
0
0
63.95
2.33
0
0
0.31
0.01
0.086
0.090
0.270
0.109
CR
0.527
Distance to
fault (m)
0.056
(1) 0–500
1,885,370
30.83
38,700
50
0.35
0.641
500–1000
1000–1500
>1500
1,125,372
474,307
2,631,091
18.4
7.76
43.02
22,500
7200
9000
29.07
9.3
11.63
0.34
0.26
0.06
0.221
0.086
0.050
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Table 2. Cont.
Factors & AHP
Weights
Classes
Pixels of Each
Class
% of
Pixels
Landslide
Pixels
% of
Pixels
FR
AHP
Weights
CR
0.03
Distance to
drainage (m)
0.085
<100
13,05,283
21.34
35,100
45.35
0.42
0.41
100–200
200–300
>300
1,097,497
947,452
1,999,494
17.94
15.49
32.69
19,800
9000
9000
25.58
11.63
11.63
0.28
0.15
0.07
0.254
0.152
0.078
Slope ◦ (%)
0.212
0–10
10–20
20–30
30–40
>40
396,204
986,022
1,593,420
1,696,257
1,432,378
6.49
16.15
26.1
27.79
23.47
900
5400
10,800
23,400
36,900
1.16
6.98
13.95
30.23
47.67
0.04
0.1
0.13
0.25
0.48
0.053
0.067
0.235
0.325
0.320
Elevation (m)
0.184
<1000
1000–3000
3000–4500
>4500
12,841
2,662,889
2,127,205
1,313,227
0.21
43.54
34.78
21.47
0
67,500
9900
0
0
87.21
12.79
0
0
0.85
0.16
0
0.067
0.147
0.493
0.291
Aspect
0.141
Flat
North
Northeast
East
southeast
South
Southwest
West
Northwest
North
201
358,109
704,069
708,560
753,766
799,804
854,910
814,385
752,598
357,879
0
5.87
11.53
11.61
12.35
13.1
14.01
13.34
12.33
5.86
0
1800
4500
5400
16,200
21,600
18,000
5400
3600
900
0
2.33
5.81
6.98
20.93
27.91
23.26
6.98
4.65
1.16
0
0.05
0.06
0.07
0.21
0.26
0.21
0.06
0.05
0.02
0.064
0.047
0.051
0.071
0.014
0.016
0.018
0.015
0.08
0.062
0.032
0.158
0.006
0.092
Distance to
roads (m)
0.032
<50
169,279
2.77
4500
5.81
0.21
0.061
50–100
100–150
>150
163,787
158,579
5,624,495
2.68
2.59
91.96
9000
5400
58,500
11.63
6.98
75.58
0.44
0.27
0.08
0.095
0.315
0.527
0.07
Lithology
0.101
Biotitie schist, Kynite
gneiss
Glacio-Fluvial deposites
Granitic_Gneiss and
Granitoid
Micaceous sandstone
Pale white to Green
Quartzite
Pebbly siltstone
Phyllite Quartzite, Basic
Flows
Quartzite Schist
Slate phyllite
Sreaky banded gneisss
Wangtoo Granite
phyllite
phyllite schist
purple Limestone
99,355
1.63
0
0.00
0.00
1517
0.02
0
0.00
0.00
0.043
0.042
114,826
1.88
0
0.00
0.00
0.045
1,122,715
18.38
39,600
51.16
0.17
0.252
151,271
2.48
0
0.00
0.00
0.073
134,854
2.21
2700
3.49
0.10
0.07
12,408
0.20
0
0.00
0.00
0.083
206,344
957,212
203,907
2,407,184
299,137
156,698
239,881
3.38
15.67
3.34
39.41
4.90
2.57
3.93
2700
1800
5400
6300
1800
15,300
1800
3.49
2.33
6.98
8.14
2.33
19.77
2.33
0.06
0.01
0.13
0.01
0.03
0.46
0.04
0.064
0.058
0.049
0.053
0.047
0.063
0.052
0.016
4. Results and Validation
To produce the susceptibility map, three different methods were used, for which the methods’
output values were derived through GIS spatial analysis and data aggregation models [60]. Figure 3
shows the results of the LSM obtained from three methods. The natural breaks classification method
used in this study generates classes of similar values separated by breakpoints. This is a common and
effective method for categorising potential mapping results when we interpret values close to each
class boundary, e.g., values between “Low” and “Moderate” probability [61].
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To generate the LSM maps and identify the areas highly susceptible to landslides, the criteria
weightings derived from three methods were used for data aggregation within a GIS environment.
Figure 3a–c presents the LSM results. The natural breaks classification method applied in our study
generates classes of similar values separated by some breakpoints. To validate the resulting LSMs and
identify the improvement in accuracy with using sensitivity analysis, a receiver operating characteristic
(ROC) curve was used for validation.
Figure 3. (a) Landslide inventory and output landslide susceptibility maps for each method, (b) AHP,
(c) FR, and (d) hybrid SMCE.
Validating the training dataset is a very important step for a susceptibility analysis along with
a receiver operating characteristic (ROC) plot to determine its prediction rate [62]. The ROC is a method
of estimating the prediction rate, and has been widely used by landslide hazard experts [13]. A value
range of the ROC curve between 0.5–1 shows a good-fit, while ROC values of under 0.5 represent
a random fit [60].
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A total of 105 landslide locations (70%) were used for training LSM methods, and 44 landslide
locations (30%) were used for validation purposes. The accuracy of each applied method was also
measured by comparing the resulting susceptibility maps with the observed landslides. Calculating
the area under the ROC curve is a common approach for estimating accuracy of the occurrence or
non-occurrence of predictive methods. In this research, ROC curves were obtained by means of
statistical analysis software. The ROC curves of the evaluation for the three resulting susceptibility
maps based on the different methods of AHP, FR and hybrid SMCE are shown in Figure 4. The resulting
ROC values for AHP, FR and hybrid SMCE were 0.797, 0.907 and 0.910, respectively. According to
the results, the FR method seemed to be a more accurate landslide susceptibility prediction method
compared to the other two methods. Enlarged sub-areas from the resulting landslide susceptibility
maps are presented in Figure 5 for an overview of the results.
100
80
70
60
AHP, AUC=0.797
50
FR, AUC=0.907
40
Hybrid SMCE, AUC=0.910
30
20
10
0
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
101
True positive rate (%)
90
False positive rate (%)
Figure 4. Results of ROC plots for the produced susceptibility maps.
Figure 5. Cont.
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Figure 5. An enlarged sub-area from the resulting LSMs generated based on three different models (a)
AHP, (b) FR, and (c) hybrid SMCE.
5. Discussion
GIS-based statistical modelling is a powerful and essential tool for assessing and mapping
landslide susceptibility. In previous studies, AHP, FR and hybrid SMCE methods were used either
separately [25,63] or compared with other landslide susceptibility methods. In this paper, we compared
the three above-mentioned GIS-based methods, which has never been done in the context of LSM in
the higher Himalayan domain. The FR method proved to be simple and easy to apply in the highly
rugged topography of the Himalayas.
In contrast, the hybrid SMCE method appeared complex, and the AHP method proved to be more
complex where domain expert knowledge is required for giving weight to factors [64]. The FR and
consequently the hybrid SMCE methods enable the evaluation of relationships between a dependent
and several independent variables only in a discrete form.
On the other hand, the AHP allows evaluating the continuous independent variables in addition
to distinct forms [65]. The three landslide susceptibility maps produced as a result of this study show
a different spatial distribution of the zones that are highly susceptible to landslides. The FR and hybrid
SMCE methods gave very similar results. In some areas, the AHP method map shows significant
variations compared to the FR and hybrid SMCE maps. This is mainly the case in the northern and
eastern parts of Kullu valley (Figure 3). Only 45% of the very high susceptibility class overlapped
in all methods. To verify the results of the three landslide susceptibility methods, we carried out
a comparison using the area under the curve (AUC) of the success rate curve (SRC). AUCs of the SRC
plot suggest a similar efficiency for the LSMs obtained from the FR and hybrid SMCE methods with
values of 0.907 and 0.910, respectively. Only the AHP based LSM showed a significantly lower AUC
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of 0.797 (Figure 4). The FR method shows almost identical ROC curves (Figure 4). The AHP, FR and
hybrid SMCE methods are comparatively good estimators for the LSM.
Nonetheless, all the methods produced similar accuracies, and the choice of method is less
important than a good set of predictors. The selection of appropriate factors and modelling approaches
plays an important role in obtaining results with a higher AUC [66,67]. Considering relevant factors is
required to assess the weightings of factors according to specific locations, especially for AHP and
hybrid SMCE. All three methods show that the lithology, distance to fault lines and the terrain slope
are more effective controlling factors than other factors in the LKTW domain. This is due to differences
in the cohesion and permeability of the rock types, fault joint planes and the Earth’s gravitational [68].
Moreover, the slope aspect and landforms play an important role on this phenomenon in the Kullu
valley because they are the factors that control the effects of wind and rainfall and the exposure to
sunlight during the daytime [50].
Our study agrees with most landslide studies in the global aspects; namely that there is
a correlation between landslide distribution and lithological units. Therefore, different lithological
units exhibit different behaviours regarding landslides. Consequently, variations in the lithological
units and fault lines in the Kullu valley area are considered to have important roles, controlling the
occurrences of landslides. The more stable units are the Wangtoo granites, streaky bent gneisses
and sandstone meta-sediments. These are highly water permeable units, which reduce landslide
occurrences. The fieldwork observations show that the weathered low-grade meta-sedimentary and
clastic rocks, e.g., mica schist, phyllites, quaternary alluvium, limestone, siltstone, etc. show similar
behaviour to soil material. The presence of soils rich in clay minerals makes the terrain slopes less stable.
Many landslides within the Kullu valley area occur within the phyllite, mica schist and limestone
rocks. However, results in Reference [1] show that these rocks are affected by many sets of joints and
fractures, which may facilitate water infiltration as well as weathering and create sliding.
6. Conclusions
For the first time, the hybrid SMCE method is applied as an integration of the FR and AHP
methods to compute the related weightings regarding landslide susceptibility for the Kullu valley,
LKTW, Higher Himalayan region, India. This integration method has not been evaluated previously
for the north-western Himalayan terrain, and we thus we attempted to determine their accuracy
assessment in LSM. Two of the conditioning factors (i.e., lithology and slope aspect) have more
influence than other factors on landslides occurrence. This study demonstrates that the factors of
landforms and distance to lineaments have a more useful impact on the resulting susceptibility
mapping than other factors such as land use, land cover and slope curvatures. The distance to fault and
distance to lineaments layer contributed to an increase in AUC for the FR and hybrid SMCE landslide
susceptibility maps. Our results indicate that the FR and hybrid SMCE methods yield similar results
for Kullu valley, while the AHP method is less accurate for LSM. The hybrid SMCE and FR methods
give the overall higher prediction accuracy for the Kullu valley area. The error and the variability
associated with the integration method of the hybrid SMCE and FR are less than AHP method when
used separately for the LSM. However, the FR method has an advantage of implementation simplicity
compared to the other applied methods in this study. For our future work, we aim to develop GIS-based
data mining techniques using machine learning methods for landslide susceptibility modelling and
mapping in this study area.
Author Contributions: Conceptualization, S.R.M. and S.T.P.; data curation, B.K.M.; funding acquisition, S.R.M.
and S.T.P.; investigation, S.R.M.; methodology, S.R.M.; validation, S.R.M.; visualization, S.R.M.; writing—original
draft, S.R.M.; B.K.M. and S.T.P.; writing—review and editing S.R.M. and S.T.P.
Funding: This research is partly funded by the Austrian Science Fund (FWF) through the GIScience
DoctoralCollege (DK W 1237-N23).
Conflicts of Interest: The authors declare no conflict of interest.
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