Application of Multi Criteria Decision Making Techniques on Landslide Hazard Zonation mapping

Abstract

Decision making is one of the subfields of Artificial Intelligence and has been used for various applications. One such application is Disaster Mitigation and Management. This study is on Landslide Zonation using different Decision Making Algorithms. Landslides are one of the most epoch-making hazards that affect different parts of India every year, during the rainy season, which cause not only colossal destruction to roads, bridges, and houses but also lead to loss of life. Mapping of the hazardous zones is one method by which awareness can be created among the localities and guiding the authorities in management of the disaster event. There are lots of literature discussing various methods to map landslide hazardous zones, from those studies, influential factors are identified and two multi criteria decision making algorithms namely TOPSIS and AHP are used to rank the factors, to assign weights to corresponding factors, finally Landslide Susceptibility Map of Saklespur, located in Western Ghats, is created with the help of Geographic Information System Software. As a result, Landslide hazard zonation map is created on considering different weights such as Land use Land cover and Precipitation around 22%, Distance from road and Lineament Density 14%, Drainage Density 5% Slope and Elevation around 5%, Geology around 4% and Aspect around 2%. From the results of the method, it is clear that human efforts in understanding the nature is needed to be increased to save the world.

Full text

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Application of Multi Criteria Decision Making
Techniques on Landslide Hazard Zonation mapping
Sreedevi Narayana
Vellore Institute of Technology
Karthikeyan Jayaraman (  karjk2017@gmail.com )
Vellore Institute of Technology
Research Article
Keywords: Decision Making Algorithms, Disaster Management, Landslides, Analytical hierarchy process,
TOPSIS technique
Posted Date: July 20th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-3058785/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

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Abstract
Decision making is one of the subelds of Articial Intelligence and has been used for various
applications. One such application is Disaster Mitigation and Management. This study is on Landslide
Zonation using different Decision Making Algorithms. Landslides are one of the most epoch-making
hazards that affect different parts of India every year, during the rainy season, which cause not only
colossal destruction to roads, bridges, and houses but also lead to loss of life. Mapping of the hazardous
zones is one method by which awareness can be created among the localities and guiding the authorities
in management of the disaster event. There are lots of literature discussing various methods to map
landslide hazardous zones, from those studies, inuential factors are identied and two multi criteria
decision making algorithms namely TOPSIS and AHP are used to rank the factors, to assign weights to
corresponding factors, nally Landslide Susceptibility Map of Saklespur, located in Western Ghats, is
created with the help of Geographic Information System Software. As a result, Landslide hazard zonation
map is created on considering different weights such as Land use Land cover and Precipitation around
22%, Distance from road and Lineament Density 14%, Drainage Density 5% Slope and Elevation around
5%, Geology around 4% and Aspect around 2%. From the results of the method, it is clear that human
efforts in understanding the nature is needed to be increased to save the world.
Introduction
Geological, hydrological and atmospheric vicissitudes lead to calamitous destruction of ora and fauna,
articial constructions and fatalities resulting in socio economic disruption . Disaster events triggered by
nature are inexorable but the rate of fatalities can be abridged by creating awareness among the people
(Luu
et al.
2017). Frequent occurrence of disasters events are recorded lately due to the incongruous
enhancement accomplishments implemented by human life without considering Earth natural system
(Ahmad 2007). Over 1 billion of people’s life are critically affected by natural disasters during the past 2
decades due to the deciency of possessions, framework and preparedness systems (Watson
et al.
2007). Changes occurring in earth’s atmosphere and modication of topography due to human activities
results in various natural and manmade disasters (Suresh and Yarrakula 2018a, 2019). Landslides are
one among the various disasters that results in fatalities and loss of infrastructure and economy (Mata-
Lima
et al.
2013).
Among various natural disasters experienced with in the Indian extent, one of the foremost
hydrogeological hazardous event that distress major portion of India is Landslides (Kapur 2005).
Landslides are one of the most epoch-making hazards that have an impact on numerous locations of the
subcontinent mainly during monsoon season (Senthilkumar
et al.
2017). Landslides are a natural
process of earth’s life cycle that hits the mountainous regions frequently during monsoon period.
Landslides are failure of land mass down the slope affecting landscapes intimidating human life, Flora,
Fauna and non-natural structures under unpredictable climatic and lithological conditions (Shirani and
Pasandi 2019).

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Being a natural disaster existing in mountainous provinces, landslides distresses communal and
economic development, specically in emerging provinces(Suresh and Yarrakula 2018b). Himalayan
range in the northern and north-eastern of India and Western Ghats and Eastern Ghats in the southern
part of India are highly prone to landslide events resulting in casualties and economical loss(Stephen
2012). Varying topography along Himalayas and Western Ghats in India hold an astonishing historical
catastrophic landslide events over decades(Kumar and Annadurai 2013). Rainfall triggered landslide hit
Guwahati on 18th September 1948 resulting in over 500 casualties and reported that an entire village has
been destroyed by the landslide (Mandal and Mondal 2018).
In 1968, Darjeeling experienced heavy precipitation of about 1000 mm resulting in 91 landslides across a
highway stretch of 60 km resulting in death of over 1000 human life (Pal
et al.
2016). Series of landslides
hit Mapla village in Uttarakhand from 11th August, 1998 for a period of 7 days wiping out the entire
locality resulting in over 380 causalities (Paul
et al.
2000). Kedarnath in Uttarakhand experienced
landslide triggered by oods on 16th June 2013 which is reported to have over 5700 casualties and is
recorded as a devastating landslide event in the history of landslides in India (Manish
et al.
2013). In
Western Ghats of India, landslides occurrence has increased over decades resulting in increase in the rate
of casualties and damage to properties in the Nilgiris, Idukki District, Wayanad District, Kodagu District
and Western parts of Pune district (Kumar
et al.
2017). Malin landslide triggered due to heavy rainfall hit
along the Western Ghats on 30th July 2014 resulted in killing over 151 people and near 100 where found
missing post disaster (Sarvade
et al.
2014).
Saklespur is a hill station located along the Western Ghats at a mean seal level of about 956 m, which is
a tourist attraction spot well known for coffee, cardamom, pepper and areca plantations. Located along
the Western Ghats, Saklespur is prone to landslides during the monsoon season that starts in June and
ends at September. With lesser landslides events recorded compared to Madikeri, Nilgiris and Munnar
along the Western Ghats, the location failed to grasp the attention of the researchers. Frequent landslides
recorded in Saklespur has led to the damage of crop elds and for the villagers to migrate to
rehabilitation camps. Figure 1 represents the location of the study area considered in the present study.
Inuential factors and methodology from literature:
Recent studies have proven the ability of remote sensing and GIS in site suitability analysis with the aid
of various spatial information. Introduction of Analytic Hierarchy Process (AHP) paves way for the
decision makers in utilising the spatial datasets wisely in updating process of the hazard zonation maps
(Chandio
et al.
2013). Study on mapping landslide susceptibility zones was carried out using AHP and
Fuzzy logic over Izeh Basin of Iran. Results from the study concluded that over 53% of the historical
landslide event are recorded in the very high vulnerable zone proving that the methodology can be
adopted for landslide studies (Feizizadeh
et al.
2014). Nilgiris is one the landslide prone regions and a
study over the Nilgiris using AHP and spatial data conrmed that nearly 90% of the landslide events
recorded fall under hazardous zone (Suresh and Yarrakula 2018b). Thanh and De Smedt (2012)
developed a landslide susceptibility map for Luoi district, Thus Thien Hue Province, Vietnam using

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remote sensing and GIS techniques. Analytic Hierarchy Process also used in various analysis like
Precipitation (Vaishnavi et al 2017) and in studies of categorising the factors of precipitation (Vaishnavi
et al 2020). AHP based multi-criteria decision making techniques is adopted using various thematic
layers such as slope, land use / land cover, geomorphology, lineament density, lithology, elevation,
weather, distance from drainage and precipitation for the identication of landslide susceptibility zones.
As a result from the study a landslide susceptibility map for Luoi district is developed with four classes
such as low, moderate, high and very high zones respectively (Thanh and De Smedt 2012).
Investigation of disasters using remote sensing techniques paved way for periodical monitoring and
instant response during various disastrous events in both regional and global scale. Availability of
various open access datasets and open source software’s helps in intensive investigation and analyse
disaster events. There are several inuential factors suggested by various authors Landslide
Susceptibility mapping (LSM). Some of them are based on Fuzzy inference systems for ranking of
suggested parameters. Mamdani Fuzzy interface system in Matlab is one of the method to derive
Landslide Susceptibility Zone , using various topographical, geological and environmental inputs such as
Altitude, lithology, slope gradient, curvature, NDVI, SPI and the model takes 351 data points as reference
points of landslide assessment (Akgun et al 2012). Fuzzy member functions, for the purpose of pairwise
ranking of Hazard susceptibility criteria, Analytical hierarchical processing system and list of criteria used
in the study are Slope, Land use and Land Cover, Aspect, Distance to stream, Lithology, Distance to roads,
Drainage density, Distance to fault and precipitation (Feizizadeh et al 2014).
Multicriteria Decision making system like Analytical Hierarchical Processing(AHP) and OWA (Overlay
weighted analysis) are also used to do uncertainty analysis of LMS with parameters called Aspect,
Distance to road, elevation and distance to stream, distance to fault, slope, land use, precipitation
lithology (Bakhtiar et al 2013) and some other inuential parameters were analysed (Saha et al 2002). On
following these literature and with respect to the availability of data, nine of the parameters where
selected area for the study. In Karnataka region, Migmatites and granodiorite, charnockite and very few
areas of metabasalt and tuff about of lithology of Sakelspura. The study categorized Sakelespur under
good and very good ground water potential zones and slope is also categorized under moderate to high
and undulated terrain of Saklespur (Basavarajappa et al 2015).
Landslides are a natural process of earth’s life cycle that hits the mountainous regions frequently during
monsoon period. Landslides are failure of land mass down the slope affecting landscapes intimidating
human life, Flora, Fauna and non-natural structures under unpredictable climatic and lithological
conditions(Shirani and Pasandi 2019). Lithology plays a major role in identication of landslide hazard
zonation regions as the structure of the rocks and its parameters are directly related to the landslides.
Geology is utilised as a major layer for landslide vulnerability analysis and for the present study the
geology layer of 1:50000 scale is obtained from Geological Survey of India. Geology Map of Saklespur is
composed of several minerals is shown in Figure 2a and they are classied into ve divisions on
following the ow of previous literature (Basavarajappa et al 2015) from very low to very high based on
the percentage area is taken for the study. Rainfall is a primary catalyst for landslides as most of the

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landslide in India are triggered by rainfall during the monsoon season. INSAT 3D HEM Rainfall data is
collected from MOSDAC website for the period of 1st June 2019 to 31st August 2019 and the average of
the datasets are calculated and utilised in the present study. Rainfall data utilised in the present study is
shown in Figure 2b.
Topography of a region plays a major role in landslide occurrence as regions that are at never
experience landslide whereas the varying topography subjected to the external parameters such as
rainfall (Vaishnavi et al 2019) results in triggering landslides. Shuttle Radar Topography Mission Digital
Elevation Model (SRTM DEM) of the study area is obtained from USGS earth explorer at a spatial
resolution of 30m is shown in Figure 3a. SRTM DEM obtained is utilised in obtaining the slope angle of
the region as shown in Figure 3b.
Slope angle is one of the primary factors to be considered in landslide hazard zonation mapping which is
directly proportional to landslides. Slope angle integrated with the input parameters such as rainfall and
geological parameters can aid in understanding and characterising the landslide events in updating the
vulnerable zones. Aspect for the study area is developed using SRTM DEM as shown in Figure 4b and is
also considered as an instability factor based on the slope face during rainfall, sunlight and blowing
winds which directly inuence the distribution of vegetation, evapotranspiration, thickness of soil and
degree of saturation of water. Exhaustive investigation of the surface translations over the landslide
prone region guides the government ocials and researchers to serve the localities (Martha
et al.
2017).
Surveyors experienced various challenges in monitoring and assessment of landslides using traditional
surveying techniques such as tape and chain with arrows, pegs and ranging rods, plane table, etc. (Selvi
2012). Inaccuracy of results lead to the introduction of dumpy level, theodolite and total station in the
eld of surveying are time-consuming operations, which required technical manpower (Visakh
et al.
2016). Introduction of space-based surveying helps surveyors to overcome the hurdles and monitor
periodically (Geethapriya
et al.
2018). Technological development over past decades encourages
researchers understand landslide characteristics considering both geological and geomorphological
parameters (Suresh
et al.
2018).
Spatial distribution of land features and aspect are shown in Figure 4a and 4b where the land use
categories directly inuence the occurrence of landslides. Modications to the natural features based on
human requirements inuence the landslides and one of the major human activities along the hilly
regions that trigger landslides are deforestation. Land use/ land cover map for the study area is prepared
with the aid of Sentinel 2 data with Bhuvan land cover map as reference. Roads are another articially
created parameter that trigger landslides. Roads are constructed along the slope of the hills to enable the
movement of vehicles and supply the goods to the people located in the hill stations. Extraction and
cutting of the slopes for the construction of roads results in loss of satiability along the slopes due to the
vibration created by the ow of vehicles resulting in slope failure. Considering the scenario, a buffer zone
is created based on the distance from the roads as show in Figure 5a for the preparation of hazard
zonation map.

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Streams play a major role in maintaining the slope stability by increasing the water level in the soil that
results in slope instability due to erosion. Density of the stream denes the movement of water into the
soil, higher the density of streams will have higher erosion leading to slope failure. In the present study
drainage are obtained using DEM and the drainage density map is developed by using inverse distance
weighting method as shown in Figure 5b.
Lineaments are linear feature in landscape that represents the underlying geological structure such as
faults, folds, etc.  Lineament density is prepared based on the density of lineaments using IDW method
as shown in Figure 6. Lineaments are based on the geological aspects and more the lineament density in
a region the possibility of occurrence of landslides are high. In the present the study, the layers collected
are analysed and mapped using QGIS 3.10 LTR version for the identication of Landslide hazard
zonation (LHZ) regions. Based on the previous research works, discussed in the literature section, these
inuential parameters are categorized into ve, Very High, High, Medium, Low and Very Low. Percentage
area of those ve categories of inuential parameters of the particular study area boundary are
considered.
Methodology
The study attempted to ensemble two MCDM techniques to get the Land Slide Hazard assessment. First
technique is TOPSIS -
Technique for Order Preference by Similarity to Ideal Solution
(Hwang and Yoon
1981) and second one is AHP –
Analytical Hierarchical Processing (T. Saaty)
Both algorithm can be used
when many criteria contribute to a phenomenon and those criteria are to be ranked, and when their
percentage contribution is needed to be known. The ow of methodology as follows:
Algorithm:
Step 1: The dimension of Matrix is
‘M X N’
where M is number of parameters and N is number of
categories with respect to this criteria. Number of parameters utilized is nine and number of categories
are ve.
Step 2: Initial matrix
A
is framed with
M X N
elements as shown in equation (1).
Step 3: Initial matrix is scaled between ‘0’ and ‘1’ Instance of the matrix with respect to the study is
presented below as a table 1.
Table 1: Initial Matrix Aij

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Parameters VL L M H VH
Slope 0.6647 0.2628 0.0659 0.0062 0.0004
Lineament Density 0.3295 0.2576 0.2670 0.1234 0.0226
Rainfall 0.0440 0.2393 0.1708 0.3759 0.1700
Distance from Roads 0.2431 0.2171 0.1965 0.1794 0.1640
Elevation 0.0356 0.0529 0.0756 0.7647 0.0712
Drainage Density 0.0088 0.0725 0.2050 0.3052 0.4085
Land use/Land cover 0.0105 0.3523 0.5840 0.0073 0.0459
Aspect 0.1266 0.0000 0.3824 0.2544 0.2366
Geology 0.0002 0.0006 0.9580 0.0408 0.0004
Step 4: Weighted matrix is created with the help of mean calculation of initial matrix shown in table 2.
Table 2: Weighted Matrix WAij
Slope 0.1624 0.1206 0.0190 0.0018 0.0001
Lineament Density 0.0805 0.1182 0.0769 0.0362 0.0069
Rainfall 0.0108 0.1099 0.0492 0.1102 0.0517
Distance from Roads 0.0594 0.0996 0.0566 0.0526 0.0498
Elevation 0.0087 0.0243 0.0218 0.2242 0.0216
Drainage Density 0.0021 0.0333 0.0590 0.0895 0.1241
Land use/Land cover 0.0026 0.1617 0.1682 0.0021 0.0140
Aspect 0.0309 0.0000 0.1101 0.0746 0.0719
Geology 0.0000 0.0003 0.2758 0.0119 0.0001
Step 5: Next step is to nd the Positive Ideal Solution (PIS) as shown in table 3 below, the difference
between Unit matrix and weighted matrix.
Table 3: Positive Ideal Solution Matrix

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Slope 0.4836 0.5077 0.5664 0.5763 0.5773
Lineament Density 0.5309 0.5091 0.5330 0.5565 0.5734
Rainfall 0.5711 0.5139 0.5490 0.5137 0.5475
Distance from Roads 0.5431 0.5198 0.5447 0.5470 0.5486
Elevation 0.5723 0.5633 0.5648 0.4479 0.5649
Drainage Density 0.5761 0.5581 0.5433 0.5257 0.5057
Land use/Land cover 0.5759 0.4840 0.4803 0.5761 0.5693
Aspect 0.5595 0.5774 0.5138 0.5343 0.5358
Geology 0.5773 0.5772 0.4181 0.5705 0.5773
Step 6: Table 4 below shows Negative Ideal Solution (NIS) which can be derived by nding difference
between weighted matrix and Zero matrix.
Table 4: Negative Ideal Solution Matrix
Slope 0.09376 0.06963 0.01096 0.00105 0.00008
Lineament Density 0.04648 0.06827 0.04438 0.02088 0.00396
Rainfall 0.00621 0.06342 0.02839 0.06362 0.02983
Distance from Roads 0.03429 0.05752 0.03266 0.03037 0.02877
Elevation 0.00502 0.01402 0.01257 0.12945 0.01248
Drainage Density 0.00124 0.01921 0.03408 0.05166 0.07167
Land use/Land cover 0.00148 0.09334 0.09709 0.00123 0.00806
Aspect 0.01786 0.00000 0.06356 0.04306 0.04152
Geology 0.00003 0.00017 0.15926 0.00690 0.00007
Step 7: In order to nd Relative closeness, following equation (2) is used and the RC values are prioritized
in the table 5.
Table 5: Relative closeness Matrix

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Parameter Relative closeness Assigning priority
Slope 0.0608 3
Lineament Density 0.0637 5
Rainfall 0.0663 6
Distance from Roads 0.0636 5
Elevation 0.0601 3
Drainage Density 0.0616 4
Land Use/Land Cover 0.0667 6
Aspect 0.057 1
Geology 0.058 2
Step 8: To prove the stability of the priority assigned, analytical Hierarchical processing is used and the
following table 6 shows the priority Matrix for the case study and this acts as a base matrix.
Table 6. Comparison Matrix
Parameters A G E S DD DR LD R LU/C
Aspect(A) 1.00 0.50 0.33 0.33 0.25 0.20 0.20 0.17 0.17
Geology(G) 2.00 1.00 0.50 0.50 0.33 0.25 0.25 0.20 0.20
Elevation(E) 3.00 2.00 1.00 1.00 0.50 0.33 0.33 0.25 0.25
Slope(S) 3.00 2.00 1.00 1.00 0.50 0.33 0.33 0.25 0.25
Drainage Density(DD) 4.00 3.00 2.00 2.00 1.00 0.50 0.50 0.33 0.33
Distance from Roads(DR) 5.00 4.00 3.00 3.00 2.00 1.00 1.00 0.50 0.50
Lineament Density(LD) 5.00 4.00 3.00 3.00 2.00 1.00 1.00 0.50 0.50
Rainfall(R) 6.00 5.00 4.00 4.00 3.00 2.00 2.00 1.00 1.00
Land Use/Land Cover(LU/C) 6.00 5.00 4.00 4.00 3.00 2.00 2.00 1.00 1.00
Step 9: As a next step of AHP, Weighted matrix as shown in table 7 is derived from base matrix,
Table 7. Weighted Matrix

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Parameters A G E S DD DR LD R LU/C
Aspect 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.04 0.04
Geology 0.05 0.03 0.02 0.02 0.02 0.03 0.03 0.04 0.04
Elevation 0.08 0.07 0.05 0.05 0.04 0.04 0.04 0.06 0.06
Slope 0.08 0.07 0.05 0.05 0.04 0.04 0.04 0.06 0.06
Drainage Density 0.11 0.11 0.10 0.10 0.07 0.06 0.06 0.07 0.07
Distance from Roads 0.14 0.15 0.15 0.15 0.15 0.13 0.13 0.11 0.11
Lineament Density 0.14 0.15 0.15 0.15 0.15 0.13 0.13 0.11 0.11
Rainfall 0.17 0.18 0.21 0.21 0.23 0.26 0.26 0.23 0.23
Land Use/Land cover 0.17 0.18 0.21 0.21 0.23 0.26 0.26 0.23 0.23
Step 10: Consistency Ratio of the matrix shows how the matrix is reliable and it can be computed by
dividing Consistency Index (CI) which can be found using equation (3) stated below and Radom Index
(RI) which is suggested by T.L. Saaty as shown in the table 8.
Random Index varies with respect to number of parameters utilized in the study. According to our study,
there are nine parameters, so the value can be considered as 1.45.
Table 8. Random Index values obtained from Saaty’s book (Source: Saaty, 1977)
n 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49
Step 11: According to Saaty, the Consistency Ratio (CR) with is computed using equation (4) shows the
result around 0.01. The study also reveals the value closer to 0.01 which is 0.015. If the CR value closer to
0.01, it can be claimed that the percentage contribution values can be stable and reliable.
Step 12: The percentage contribution is calculated from Resultant matrix which is shown in table 9.

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For Land Use/Land Cover and Rainfall layers, 22.49% is the weightage given and Lineament Density &
Distance from Roads hold the contribution weightage 14.13%, Drainage Density hold 8.99%, Slope &
Elevation hold 5.70%, Geology holds 3.72, and Aspect has 2.60%. Landslide Susceptibility Mapping is
created with the weights revealed from the above methodology.
Table 9. Resultant Weightage derived from AHP
Parameters Weightage
Land Use/Land Cover 22.49
Rainfall 22.49
Lineament Density 14.13
Distance from Roads 14.13
Drainage Density 08.99
Slope 05.70
Elevation 05.70
Geology 03.72
Aspect 02.60
Results And Discussion
Few of the factors are chosen from the literature and they are categorized into ve divisions based on
their intensity of causing Landslide. The idea of categorization is adopted from Fuzzy (Zheng et al)
models. The percentage area occupied in the study area by certain category is considered for further
calculation. In order to rank the factors, TOPSIS method is utilised and to know the percentage of
contribution AHP is used. Based on the Relative Closeness value in TOPSIS, the factors are assigned
certain relevancy and as a next step relevance matrix is formed. On utilizing AHP Methodology, weights
of parameters are shown in the table.
As per the value of resultant matrix of AHP, LULC and Rainfall has higher contribution over Lineament
Density and Distance from Roads, Drainage density holds a bit more values when comparing Slope and
Elevation nally Geology and Aspect are given Minimum.
Following gure 7 shows the points of historic data where landslides occurred and it also acts as a
validation part of the work (i. e) though the validation points are limited in the study area, all the points lie
along the High susceptibility zone of LSM. This proves the methodology has the capacity to build a
feasible solution with the limited resources and data.
Future work:

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According to the basics of optimizing technique, the maximum results should be achieved with the
available data, tools and methodologies. As the future part of this research work, some machine learning
algorithms are to be included to tune the results further with more parameters and spatial validation for
this study are also to be done by applying similar methodology to different area of studies with high
resolution of data.
Declarations
Ethics approval and consent to participate
Not Applicable
Consent for publication
Not Applicable
Availability of data and materials source -Data in Brief
1. Geology is utilized as a major layer for landslide vulnerability analysis and for the present study the
geology layer of 1:50000 scale is obtained from Geological Survey of India.
2. Rainfall is a primary catalyst for landslides as most of the landslide in India are triggered by rainfall
during the monsoon season. INSAT 3D HEM Rainfall data is collected from MOSDAC website for the
period of 1st June 2019 to 31st August 2019 and the average of the datasets are calculated and utilised in
the present study.
3. Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) of the study area is obtained
from USGS earth explorer at a spatial resolution of 30m to generate Topography, Slope map, Aspect map,
Drainage map.
4. Land use/ land cover map for the study area is prepared with the aid of Sentinel 2 data with Bhuvan
land cover map as reference. . Land use/ land cover map for the study area is prepared with the aid of
Sentinel 2 data with Bhuvan land cover map as reference.
All of these data are open source data.
Acknowledgements
I would like to acknowledge my institute and department heads for facilitating me to carry out this
research work.
Compliance with Ethical Standards:
Conict of interests

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The authors declare that they have no conict of interests
Funding Information
Not applicable
Research involving human participants and/or animals
Not Applicable
Informed consent
Not Applicable
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Figures

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Figure 1
Geographical location of the study area

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Figure 2
a) Geology Map of Saklespur b) Rainfall Map of Saklespur

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Figure 3
a) SRTM DEM of Saklespur. b) Slope Map of Saklespur

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Figure 4
a) Land use/ Land cover Map of Saklespur, b) Aspect Map of Saklespur

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Figure 5
a) Drainage density of Saklespur b) Distance from Roads

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Figure 6
Lineament density of Saklespur

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Figure 7
Landslide Susceptibility zones of Saklespur using TOPSIS & AHP