GIS-Based Study To Develop Landslide Susceptibility Zonation Map Of District Mandi, Himachal Pradesh

Rising Incidents of landslide at district Mandi is issue of concern in Himachal Pradesh. Every year many people losses their life and property in these landslide event. This study is conducted with aim to preparation of landslide susceptibility zonation map of district Mandi using method of frequency ratio. Causative factor of landslide involved in preparation of Landslide susceptibility zonation map is Lithology, Slope, Drainage density, Aspect and Land use land cover. Slope, Drainage density, Aspect map are extracted through digital elevation model. Source of Digital elevation model used here is based on SRTM data whereas lithology map is based on data of geological survey of India. Land use land cover map is extracted by images of Landsat 8 satellite. Total of 52 existing landslides are used to model nal map. LSZ map show 40.42% area is falling under medium susceptibility class, 34.5 % under low and 25.07% is under high susceptibility class which cover tehsils Mandi, Chachyot, Thunag and some part of Padhar, Aut and Bali Chowki. Further to validate these result areas under curve (AUC) method is use which give prediction rate of 76.06%. Zonation, Areas Curve, System, TIROS: Television LSI: USGS: OLI: Land Imager, TIRS: GPS: System, FR: Frequency Ratio, RF:


Introduction
Event of landslide at mountain region of district Mandi, Himachal Pradesh causing high number of casualties every year. Prevention, Mitigation and Preparedness are the techniques advised to use by Himachal Pradesh state disaster management authority in case of any disaster. Role of vulnerability mapping is highly recommended to be used in mitigation process which is done by producing landslide vulnerability zonation map with help GIS software. Landslide hazard zonation is method of representing probability of future landslide in spatial format. Accuracy of landslide hazard zonation map depend on amount of data collected, quality of data and method of modelling (Guzzetti et al., 2005;Soeters & Westen, 1984). The preparation of landslide hazard zonation map is based on some assumptions as future landslide are more likely to occur at similar conditions which cause landslide at past, both remote sensing and eld survey can be used to map landslides, It is possible to map causative factors with applications of remote sensing and GIS (Dai & Lee, 2002;Statistical et al., 1991;Varnes, 1958). P. Aleotti, R. Chowdhury has distinguished modelling method landslide susceptibility map broadly into two categories as qualitative and quantitative approach (Chowdhury, 1999). Individual judgement plays crucial role in qualitative approach which can be based on eld survey or satellite images observation (Chowdhury, 1999;Paper, 2012). Quantitate approach is more of scienti c and reliable method (Vakhshoori & Zare, 2016).
Quantitative approach is distinguished further as Statistical Analysis, Geotechnical Engineering approaches, Neural network analysis (Chowdhury, 1999). Geotechnical engineering which is also termed as deterministic approach by some authors include preparation of mathematical model. Preparation of such model require different geotechnical properties of soil from speci c location due to which this approach is not practical for large area (Paper, 2015). Statistical method on other hand is incredibly useful in large area mapping. Due to innovation in remote sensing technology large number of data is available from different satellite like landsat8, carto sat 2, TIROS, NOAA and SANTINEL satellite etc. as innovation in GIS technology occurred is last decade so the popularity of statistical approach in landslide hazard assessment (Paper, 2015). Statistical approach is further classi ed as Bivariate statistical analyses and Multivariate statistical analyses. Bivariate statistical analyses are much related with comparing every causative factor with past landslides with aim to give individual weightage to each class of factor (Paper, 2015). Some common method used and compared by method of AUC by different researcher in this category are logistic regression (Paper, 2010 (Parkash, 2015). This west and northwest part include states Himachal Pradesh, Uttarakhand, Jammu and Kashmir. Due to increase in event of landslide and effect to social and economic condition several researchers inspired to conduct case studies and assessing landslide event is these region (Bhardwaj et  work to improve risk communication and early warning system (Fennessy & Mikolajczak, 2016). Although all these efforts are made but future possible landslide event still possess threat to new and existing settlements in this region. GIS bases study and landslide hazard zonation provide scienti c base for decision maker. Therefore, work to prepare landslide susceptibility zonation map of this region become most crucial requirement for ensuring safe development of this region. Aim of this study is to prepare landslide susceptibility zonation map of district Mandi, Himachal Pradesh using frequency ration approach.to achieve this objective list causative factor for landslide is identi ed and region is distinguished into different classes of each factor. Frequency ratio of each class is calculated so that nal map can be prepared. aim of this landslide susceptibility zonation map is to help decision maker to identify locations with most requirement of landslide mitigation measure and this study will also help future planning for land use pattern.

Material And Method Study Area
Study area covered is district Mandi of state Himachal Pradesh, India. Himachal Pradesh is hill state of India which entirely fall on Himalayan mountain range. Himachal Pradesh is located at northen part of country whereas Mandi is situated at centre of Himachal Pradesh as in Fig. 1 (a) District of Mandi rests between 31 0 13"50'N to 32 0 04"30' N latitude and 76 0 37"20'E to 77 0 23"15' E longitude. Lesser Himalayan range cover maximum part of district. During months from July to September Heavy rainfall is observed in maximum are and medium snowfall during months October to march at higher elevation. Evevation show variation from 478m to 3346m as shown in Fig. 1(b). Jogindernagar and Sarkaghat tehsils witness maximum rainfall in district(P. Singh et al., 2008). Major type of rocks present is district are Shah, Tertiary, Jutog and Chail group of rocks were each group having number of Formation and Lithology, which distinguish it in total of 32 lithology class. Beas and the Satluj are two main rivers in district which are having number of tributaries locally known as khad. Topography of region is rugged and having steep slope. Water holding capacity of soil is poor as maximum soil is of argillaceous origin. Further detailed information on method used for preparation of LSZ map and collection of material (different thematic and landslide map) is provides in following subsections.

Frequency ratio method for landslide susceptibility zonation
Landslide suseptibility zonation map is developed using friquency ratio method. Aim where is to develop model showing likelihood of landslide occorance at mandi district using current occored landslide data. Final landslide susceptibility index is calculated using rastor calculator in ArcGIS 10.3 toolbox, which can be represented mathematically as.

Data sources and layer preparation
To prepare LSZ map it was important to nd exact location of landslide occurrence which was possible by using GPS system and google earth software. journey was made at different location of Mandi to know exact location of landslide two of which is shown in Fig. 2 and validated by using google earth software.
Location which was not assessable only remote sensing data was used. To understand factor of landslide repeated eld visit was made. Different factors responsible for landslide at district was selected based on previous work and eld observation. Factor which we used in our study was Slope, Aspect, Lithology, land use land cover and Drainage density. Map of different in uencing factors has been downloaded and used for further process on ArcMap 10.3. SRTM data was used for extracting DEM tiles of study area which is made available by USGS shown in Table 1. Mosaic tool was used for combining all available tiles of and prepare one common tile. From where DEM data for study area was extracted using clip function of ArcGIS. Slope, aspect drainage density data was extracted using DEM map while using different tools on ArcGIS 10.3. Lithological map of area is obtained from GSI. All the raster was resized to same cell size by using resample tool. Every raster was classi ed into different class. Pixel count for different classes in each factor and pixel count of landslide in each class is obtained. Which was further used to obtain RF, RF and PR values. Main objective of nding Frequency ratio is to give weightage to each class and use prediction rate to give individual weightage to each factor. All the raster layers are submerged together by using raster calculator tool and LSZ map is obtained at last. Final map obtained divide area into three different classes of susceptibility of landslide as low, medium, high class. This obtained map is veri ed by observing landslide location and obtained zones. Total of 52 landslides were observed and considered in our study which is having 17.73 km 2 area whereas total study area is 3666.5 km 2 .  Fig. 3 (b). Aspect term in GIS is generally used to show orientation of slope face which may be start from north and further follow all compass directions. Aspect of slope generally in uence factors like sunlight strike in slope, growth of vegetation, soil moisture in indirectly effect occurrence of landslide. Aspect of Mandi district is obtained from DEM by using aspect tool and reclassi ed into nine classes as north, northeast, south, southwest, east, southeast, west, northwest and at see Fig. 3(a). Drainage density is perimeter which show weather area is draining water e ciently or not. It can be found by dividing total stream length to total area is that particular drainage basin. It is reclassi ed into three classes low medium and high.it is generally extracted by application of line density tool as in Fig. 3 (c) Lithology and LULC Lithological area of Mandi is divided into 32 classes by geological survey of India, all of these classes has been used in our work as shown in Fig. 4   Authors' Contributions Sachin Verma was major contributor in every step. He was involved in eld survey, collection of dataset and various map, preparation of susceptibility zonation map using FR approach and writing of manuscript. Vidya Sagar Khanduri play major contribution in testing model using AUC and approved nal the manuscript.

Availability of data and material
Dataset used in this study for analysis purpose can be provided if asked by author. Figure 1 (a) location map of study area (b) DEM image of study area. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.

Figures
Page 16/19    Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.