Risk Resilience of Growing Settlements in Landslide Prone Hilly Areas: Case Study on Kalimpong-I Block, Darjeeling District, West Bengal

Risk Resilience of Growing Settlements in Landslide Prone Hilly Areas: Case Study on Kalimpong-I Block, Darjeeling District, West Bengal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk Resilience of Growing Settlements in Landslide Prone Hilly Areas: Case Study on Kalimpong-I Block, Darjeeling District, West Bengal Chalantika Laha, Shovanlal Chattoraj, Ganga Prasad Prasain, Poonam Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3676394/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 May, 2025 Read the published version in Natural Hazards → Version 1 posted 4 You are reading this latest preprint version Abstract Darjeeling-Sikkim Himalaya is a hotspot of landslide occurrences in India. Losses of natural and human resources has become common and frequent news for this area as an effect of landslide. At the same time, it’s a very potential zone from developmental and tourism perspective which leads to emerging population growth and settlement expansion. The directional magnitude of this sprawling depends on the physical, environmental and infrastructural strengths of the area. But this can be threatened by landslide. Hence, to minimize loss of lives and property, optimization and restriction of developmental activities in highly sensitive areas is the need of the hour. Kalimpong is a highly sensitive site for such issue for its emerging urban agglomeration. Hence, the case study was conducted in Kalimpong-I block in Darjeeling District. Quantitative simulation by multivariate logistic regression was carried out based on influencing factors and landslide inventory data for landslide susceptibility analysis. Digital elevation model (DEM), Landsat-8 OLI satellite imagery and also some secondary data were used to generate the individual spatial database to formulate dependent variables. Spatial overlay analysis with the final outputs for predicted urban sprawling and predicted landslide occurrence zones enabled the managing authority to identify future highly vulnerable zones as well as the safer zones for settlement and infrastructure expansion. This helped the authority to restrict the set-ups resulting minimization of elements at risk. It can help in the disaster preparedness as well as mitigation planning. Therefore, this study shows a holistic approach towards effective disaster management and risk resilience. landslide susceptibility analysis settlement expansion vulnerability assessment risk resilience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. INTRODUCTON Landslide is a large-scale landmass instability, triggered by slope imbalance and reduced resistance. Every year, it causes not only the collapse of hundreds of houses and other infrastructures but also the depletion of natural resources and lives. Eventually, it escalates into a natural disaster which causes devastation in significant amount and overwhelms the capability of people to adapt and avoid. The majority of this kind of natural disasters take place in rocky terrain. The high earthquake prone zone primarily encompasses these landslide-prone regions in the Himalayan topography (GSI report 2014). Froude and Petley, ( 2018 ) highlighted the Indian Himalaya is one of the largest landslip hotspots and accounts for 16% of the world's rainfall-induced landslides. The majority of slope instability is caused by human activity, including quarrying, construction, and illegal hill-cutting etc. Researchers of University of Sheffield, England, reported that country-wise the most construction triggered land slide events occur in India i.e. 28% (Froude and Petley 2018 ). According to GSI report (2014), Darjeeling Himalaya within the territory of West Bengal experienced 52 landslides only in 2014, whereas a total of 841 landslides occurred during 2000–2014 (GSI Report 2014). GSI (2016) reported 87 landslides on 1st July, 2015. Darjeeling Himalaya is characterized by complex geological, geomorphological, and seismo-tectonic configuration. The Darjeeling region's hill ranges are extremely rough, structurally controlled, and constantly undergoing highly active denudation processes. The Darjeeling and Sikkim Himalayas' mountainous regions are part of the Himalayas in terms of geology of the Himalayan Fold-Thrust Belt (FTB) in motion. These complicacies are what cause the instability of the ground. The hilly settlement areas of Darjeeling Himalayan region have been growing fast for rapid population growth. Unplanned settlement expansion triggers the damage of lives due to landslides. Hence, a plan to restrict settlement expansion in landslide prone areas will be effective to prevent loss of human resources. For the purpose, the prediction of landslide occurrences i.e. landslide susceptibility analysis is necessary based on their influencing factors. Along with this, prediction on future settlement expansion based on favorable criteria also should be conducted, so that the preventive planning can be chalked out. Remote sensing plays an important play a part in creating a map of landslides and other maps with a thematic focus on landslip occurrences (Kalantar et al., 2020 ). An integrated approach showcasing the landslide hazard vis-à-vis settlement from an urban growth perspective is relatively novel idea (Ghosh et al., 2011 ). The spatial decision making in GIS platform involves the layering of different landslide-influencing factor layers with their weighted contribution or contribution ratio to arrive at a final conclusion. There are various methodologies all over the world to calculate weightage of individual influencing factors. Some of them are based on real occurrence data whereas some are not. Among these, most commonly used are Analytical Hierarchical Process (AHP), Frequency Ratio, Information Value, different statistical prediction models like Support Vector Machine (SVM), Artificial Neural Network (ANN) etc. Simulations based on the real occurrence data are more acceptable because of its testing and training-based algorithm. Logistic regression is one such extensively used statistical prediction model (Ercanoglu and Temiz 2011 ; Lee and Pradhan 2007 ; Bai et al. 2010 ; Das et al. 2010 ; Oh and Lee 2011 ; Pradhan, 2010 ; Ohlmacher and Davis 2003 ) where, The entire computation is based on the trend line equation, which is produced by the correlation between organized causal components and actual occurrences in a binary response pattern. Based on the parametric values in the training sites, probability is estimated here using regression trend line analysis followed by the identification of various probable zones for future occurrences. Hence, this logistic regression model was implemented to retrieve the contribution of each triggering factor for predicting the future landslide prone areas (Nandi and Shakoor 2009 ; Duman et al. 2006 ; Nefeslioglu et al. 2008 ; Pradhan 2010 ) as well as the future settlement expansion (Salem et al. 2019 ; Liu et al. 2015 ; Sarkar and Chauhan 2020 . Most of the studies involving landslide susceptibility analysis considered NDVI, Lithology, soil, slope, aspect, distance to fault, distance to road, distance to drainage, elevation, TWI, distance to ridges, precipitation, elevation, etc., but some researchers used fault density, road density, or drainage density instead of using the approach as spatial buffering (Shahabi & Hashim 2015 ; Das et al. 2010 ; Feizizadeh et al. 2014 and Achour et al. 2017 ). While Yesilnacar & Topal ( 2005 ) took into account plan curvature and profile curvature, Marjanovi et al. (2011) focused on slope length rather than slope angle. Inputs included the surface area ratio, the stream power index (SPI), and sub-watershed basins. In addition to other common parameters, Regmi et al. ( 2010 ) also considered flow length, flow accumulation, tangential curvature, and solar radiation. The factors used as input affect the model's performance as well. Numerous studies used various influencing parameters behind the expansion of settlements too. As this study is focused on the hilly terrain, the factors triggering the settlement expansion should be very much terrain specific. The major factors taken by the researchers are slope, aspect, Drainage density, land use, Proximity to facilities like transport, water supply, power, commercial and administrative services, etc. (Wang and Lu 2018 ; Ren et al. 2013 ). Settlements tend to develop near the tourist viewpoints for income generation. Hence, the simulation for future settlement expansion based on such criteria can help to identify the vulnerable areas and preventive planning can be adopted. This kind of analysis is best suited for Kalimpong region which is very sensitive in terms of landslides (Ghosh 1950 ; Nautiyal 1951 ). Every year lots of land slide occurrences take place resulting losses of lives. WBDMD considers it under severely critical zone for land slide disasters. At the same time, this is the area where rapid rate of population growth and urbanization was documented (74,746 population in 2011). Population within the municipal area of Kalimpong increases from 28,885 in 1981, to 38,832 in 1991 followed by 42,998 in 2001 and 49,403 in 2011. Hence, Kalimpong region is very much suitable for such analysis and Kalimpong-I block was taken for this study (Figure-1) as this block contains the urban center of the region. This analysis can help the concerned authority to mark the zone of restrictions for settlement built-up as well as to identify the spots for disaster mitigation. 2. Data Preparation and methodology 2.1. Data preparation for landslide Susceptibility Analysis: The analysis of landslides' susceptibility is an overlay analysis based on different elements that affect the research region. In this study, influencing elements such land slope, relative relief, proximity to a road, land use and cover, lithological setup, lineament density, Normalized Differential Vegetation Index (NDVI), drainage density, soil, topographic wetness index (TWI), and aspect have been taken into account. In order to create these thematic geographical data sets, a digital elevation model with a spatial resolution of 20 m was created using spot heights and contours from the Survey of India's topographical maps of the area. Landsat-8 OLI optical satellite data was used. Secondary information was gathered from numerous sources, such as roads, soil, and lithology (Figure-2). The landslip inventory map was utilized to verify the results. Slope angle was derived from DEM as it’s a very important factor behind land slide. In the study area slope angle reaches up to 78°. Relative relief is also important factor as land slide occurs mostly in rugged terrain. It was calculated by interpolating the range of elevation data though IDW interpolation method. It ranges within the values 19 to 748 calculated for this study area. Road construction disturbs the slope stability and so distance from road impacts on the slope stability. Here, distances taken for 100m, 300m, 500m, 1km and 2km. Kalimpong-I block consists of land use / cover classes like Scrub, agriculture, agricultural plantation, Built-up, forest, forest plantation and river. Scrub, agricultural, and plantation land is extremely prone to subsidence. The rock groups found in the study region include Granite Gneiss, Quartinery Sediments, Gondwana, Daling, Siwalik, and Sikkim. Lineaments are the stratigraphy's geologically weak zones. Therefore, the likelihood of a landslip is higher where there are more lineaments. Here, the density was determined by the number of lines present. Landsat-8 OLI was used to calculate NDVI. The likelihood of a land slip diminishes as NDVI increases. The calculated NDVI of Kalimpong-I is from − 0.106235 to 0.515668. The calculated NDVI of Kalimpong-I is from − 0.106235 to 0.515668. Higher the drainage density, higher is the slope instability and soil moisture. Four types of soils are seen here. Most part of the block is covered by W002 type of soil having Siwalik and Daling group as lithological origin. These are coarse loamy soils that are overly drained and relatively shallow. Other varieties include very deep, poorly drained, coarse loamy soils (W008), moderately shallow (W004), and shallow, gravelly loamy soils (W001). Another element is the Topographic Wetness Index (TWI), which is calculated by multiplying the slope by the flow accumulation in that particular pixel. The slope failure is encouraged by the index that indicates how wet the surface is. Maximum TWI calculated for this block is 18.32. Besides all these, rainfall and tectonic activities like earthquake play very important role in land sliding. Here, rainfall was not taken in the list of factors as the study area is a very small area in terms of the available rainfall data. Variation will not be there to show its influence. Earthquake is a sudden occurrence and was not considered directly as earthquake prone zones. Rather, Lineaments were considered here which indirectly explain the tectonic activeness of the area. 2.2. Data preparation for settlement expansion Analysis: Settlement expansion in the hilly areas depends more on the physical factors than cultural. Hence for the probability of a non-settlement cell to convert to settlement cell depends on certain criteria of that cell which are slope, aspect, drainage density, land use etc. physical factors as well as association factors like Distance to road, Accessibility to tourist viewpoints, water supply, urban center etc. As settlement expansion is restricted in protected and restricted forest (as per government forest boundary) and rivers, those areas are excluded from the simulation database. Distances from road, facilities like water supply, urban center, and tourist attractions were calculated as Euclidian distances which gives us clear picture about the vulnerability of the elements which are at stake due to landslide hazard. Then they were classified to prioritize individual subclass (Figure-3). Table-1 is showing these hierarchical subclasses taken for the logistic regression. Table 1 Hierarchical order of sub-classes of individual influencing factors Distance from road (m) Accessibility to water supply Distance to Tourist attractions Distance to Urban Centers Drainage Density (DD) Aspect Slope Land use / Land Cover 1 > 3299 > 5120 > 5763 > 17200 > 4800 W 45–78 Agriculture 2 2117–3298 3424–5120 4346–5762 11761–17200 3501–4800 S 33–44 Forest Plantation 3 1466–2116 1939–3423 3501–4345 7641–11760 2401–3500 E 23–32 Agricultural Plantation 4 978–1465 636–1938 2831–3500 4450–7640 1500–2400 N 13–22 Scrubland 5 571–977 < 636 2079–2830 < 4450 < 1500 F < 13 6 245–570 1290–2078 7 < 245 < 1290 2.3. Methodology (Logistic Regression): Using this method, numbers between 0 and 1 are used to calculate the likelihood of occurrence. (Wang and Sassa 2005, Ayalew and Yamagishi 2005). The regression trend line equation, which is created based on the correlation between independent causative factors and the dependent occurrences that come from those causes, provides the foundation for the entire calculation. As a result, the model's main goal is to find the equation that best captures the relationship between the dependent variable and a set of independent parameters. (Guzzetti et al., 1999, Rowbotham and Dudycha, 1998, Tolga et al. 2005). The event's absence or presence serves as the dependent variable (y) in this analysis. The classes of the thematic data layers serve as the independent variables in this investigation. influencing either landslide occurrences or settlement expansion. The dependent variable is of binary type (occurrence data), with values of 1 (presence) or 0 (absence). If p defines the likelihood that a pixel will slide, then: p = 1 / 1 + e-z where, Z is a value from −∞ to +∞, determined by the following equation; Z = B0 + B1X1 + B2X2 +. .. + BnXn b0 is the model's intercept, n the number of causal factors, b their relative weights, and x the conditional factors. 3. RESULTS AND DISCUSSION This study analyses the future risk for the settlements which are to be built up at the fringe of existing settlement as because of their unawareness of the probability of land sliding in that area. For the purpose, first, logistic regression prediction model was implemented for the landslide susceptibility modeling. The logit equation was generated based on 11 independent variables (Figure-3) which were taken as the influencing factors for land sliding in the hilly area. The dependent variable was taken from the landslide inventory data or training set pixels. The response variable was formatted in binary format i.e. 1 for occurrence cell and 0 for non-occurrence cell. Each response cell is having 11 independent variable data. Based on the trend analysis of these variables for training pixels, the probability was retrieved for rest of the cell by logit (P) equation. As a result, the output of Logit (P) or Pred (RESPONSE) for each pixel is: Pred (RESPONSE) = 1 / (1 + exp (-(-15.1280969395951 + 10.130509342953 * TWI + 0.922165778006754 * SOIL + 0.582049096236194 * RRELIEF + 0.906193820336884 * RD_PROX + 14.7223593884136 * NDVI-1.12864977549746 * LUSE + 0.915154011933381 * LINMT_DENS + 0.924366423539928 * LITHOLOGY + 8.70632609652196 * D_DENS + 12.2723316927091 * ASPECT + 1.77835765300871 * SLOPE))) Standardized Coefficients can be seen for the individual factor with a confidence interval of 95%. Area under ROC curve is 0.917 which shows the model to be highly accurate (Figure-4). Hence, with this high accuracy logit equation, the value for each cell as the probability to slide was calculated. This was followed by the classification of total range of probability into five susceptibility zones. It can be seen that most of the land slide occurrence cells are coming under high to very high susceptible zones. This assures the reliability of the model. Another prediction was made for the future settlement expansion in this area. The research tried to build up such an awareness plan by predicting the high landslide prone areas and to coincide them with the future built up areas. As settlements are restricted to expand in forest boundaries and rivers, the area to be considered for prediction modeling excluded the areas under forest (as per govt. forest boundary) and rivers. As, only the expansion is to be granted, the exclusion of present settlement was also done. For the prediction modeling of future settlement expansion, as there is nothing like inventory data as such, so, for the validation and accuracy assessment of the model 1999 to 2011 databases was formulated as training dataset. Independent variables were taken as Slope, aspect, Drainage density, land use / land cover, accessibility to road, water supply, tourist attractions, urban centre for this area, whereas the expanded settlements in 2011 were taken as the positive response (1) variable and not expanded cells in the 1999 settlement fringe areas were taken as negative response (0) variable for generating the logit equation. Pred(RESPONSE) = 1 / (1 + exp(-(3.03280731789662–0.355741688657557 * LUSE-0.399724562258824 * SLOPE + 0.0114867641861869 * ASPECT + 0.155361539091004 * DD + 0.347824531963753 * RDDIST − 0.115459986977181 * TOURSPOT − 0.460248380960188 * URBNCNTR − 0.244915394031267 * EUCLWS))) To validate the model, this equation was implemented on 2011 data to get future probability values for each cell. The output zones were verified with the real expanded settlement in 2019. The accuracy achieved was 79.22% as this was the percentage of real occurrence cells coming under high probability zone (Figure- 5). With this accuracy, the logit equation was implemented on 2019 image to get future settlement expansion. For further analysis as per the objective of the study, the land slide susceptibility zoning raster also was subset by the forest-river mask. The range of probability values for the masked study area is from 0.000182158 to 0.978251 for landslide risk zoning and from 0.0390989 to 0.974798 for settlement expansion probability. Hence probability raster was classified into 3 prominent zones: low, moderate and high for both of the prediction modeling. As the analysis is based on zoning, the strategy behind the zone classification is very much important. In case of land slide susceptibility zonation, if the whole range of probability value is divided equally, a high frequency of data is coming under the higher range zone and the analysis will not be scientific. That’s why; classifications were made based on defined probability percentage values to keep both land slide and settlement expansion probability raster into a common and neutral platform. To make the analysis more relevant, classification was made manually to highlight the sensitive zone with probability 80% as high probable zone. The moderate probability was taken for the range of 50–80% and below 50% was taken as low probable zone. And resultant High settlement probability zones coming under different land slide sensitive zones Figure- 6 is showing the base input datasets for the main analysis of this study. These probability raster for Kalimpong-I block can help the planners to make preventive as well as mitigation panning by overlay analysis. Based on probability values in Figure-6, the major sensitive areas can be identified and are enlisted in Table-2. Table-2 Areas with High Level of probability of land sliding as an output of Logistic Regression Village wise Distribution of pixels with probability more than 80% Village wise Distribution of pixels with probability more than 90% 1 Northern Bhalukhope Northern Bhalukhope (along the road near Bhalukhope forest) 2 Northern Kalimpong adjacent to Dansong forest 3 Southern Sindibon Southern Sindibon 4 Pudung West Pudung 5 Yokprintam North Yokprintam (along the right bank of Rilli river) 6 Southern Icha Icha near brder with Kalimpong-II 7 Seokbhir Seokbhir (along major road to Lulagaon) 8 Sanralbong 9 Singi Singi (all over the village) 10 Northern and Western Lulagaon 11 Southern, Eastern and Western Samether North-west Samether (along the major road) 12 Northern and Eastern Suruk Northern half of Suruk 13 Western and Eastern Nimbong Nimbong (along the road to Samether forest) 14 Paringer Southern Paringer 15 Nobgaon Nobgaon (all over the village) 16 Sothern Lish catchment area forest 17 North and Eastern Yang Makum 18 Kaffir 19 Northern Pemling 20 Few patches in western Lulagaon forest 21 Parts of Riyong forest and Birik forest near Riyong Railway station Along NH-10 near Riyong Railway station In the report by West Bengal Disaster Management Division in 2017 ( http://www.wbdmd.gov.in/writereaddata/uploaded/DP/Disaster%20Management%20Plan%20of%20KALIMPONG.pdf ), a list of vulnerable areas was given. There, major gram panchayets with a greater number of vulnerable locations are given as: Bhalukhope, Bong, Kalimpong Khasmahal, Upper Iche, Yangmakum, Teesta, Sandepong, Pabringtar, Samether, Kaffer, Samalbong, Seokbir etc. Hence, the outcomes of this study are matching with their report except few. Teesta is not that much sensitive in the output map of this study. On the other hand, Pemling, Nobgaon, Suruk, portions of Lulagaon, Singi, Pudung was identified as prominent sensitive areas. Even, parts of Riyong forest and Birik forest near Riyong Railway station, NH-10 adjacent areas also were highlighted as sensitive areas. Moreover, the scenario in the output raster by Logistic Regression matches to a great extent with the government report. Hence, the process is reliable for such study. On 5th August, 2020, a major landslide occurred at Bhalukhope damaging the major road to Kalimpong town. In July, 2015, there were few events on NH-10 and places within Kalimpong town like 6 mile, 14 mile, and Pedong. Up to 38 people died overall, while numerous others went missing. Roads were cut off, and properties were lost. NH 10 was also got damaged resulting the cut in communication between Kalimpong and Lava. ( https://www.longdom.org/open-access/causes-of-landslides-in-darjeeling-himalayas-during-junejuly-2015-2167-0587-1000173.pdf ) A major landslide took place on NH-10 at a place called 29 mile in July, 2019. In Figure- 6, these areas are within the high-risk zone as an output of Logistic regression. Most of the land slide events affected roads and, in many cases, houses were lost. Therefore, with such risk analysis, the habitats under risk can be made aware. No more settlement encroachment in those areas should be allowed. For the purpose, the areas for further settlement expansion were simulated by the same model as of the landslide prediction i.e., logistic regression. From Figure-6, it can be identified that, settlement expansion has high probability (> 80%) in villages like Pudung, Kanke bong, Slokbhir, Lulageon, Singi, Samether, Nimbong, Nobgaon to a larger extent. whereas, Paringer, Yang Makum, Southern lowlands of Mangpong forest has the probability > 80% is few parcels. Figure-7 shows the predicted settlement cells which are coming under the high, moderate and low land slide susceptible zones. The concerned authority should restrict the development of built-ups in the highly sensitive areas first. Settlements which are tend to be emerged randomly along the minor roads connecting small habitations in the central parts of the district should be panned with utmost priority. Major areas to specially look after are: Lowlands, south of Mangpong forest. S-E and Northern part of Yang Makkum village. Eastern half of Nobgaon Khasmahal. Randomly distributed parcels in Nimbong and Paringar Khasmahal. Southern part of Lish catchment area. Northern part of Suruk. West and South of Samether. High concentration in Singi. Western part of Lulagaon. Eastern part of Slokbhir. So, these are the major areas where probability of settlement built-up as well as land sliding, both are high and should be taken care of. Figure-7 is showing the areas with a high sensitivity to land sliding within the municipal area of Kalimpong. The population in this area is growing very rapidly. In 2001, 63.5% of the block was staying in the municipal area where in 2011 it increased to 66% with a decadal variation of 14.9% during this period. Hence, the population density is ever increasing within this confined area. As in Figure: 6, it can be seen that there is a very less scope for urban expansion within this municipal area; so, the trend must be the vertical expansion. Therefore, the development of multi-storied buildings started to accommodate population growth which obviously exerts extra stress to the slope. The designing of the buildings should be kept under constant observation in the high-risk areas. SH-12 is at high risk. So, planning and management should be properly undertaken in this high population density area. On the other hand, Figure-6 is showing the safe and favorable areas for settlement expansion. Here, cells with landslide probability of less than 50% and settlement expansion probability more than 75% were overlaid. The common areas are the safe areas for settlement expansion and can be encouraged for mitigation purpose. 4. CONCLUSION Landslide is the main alarming hazard in Darjeeling-Sikkim Himalayas, annually triggered by tropical rainfall leading to sudden loss of wealth, houses and even causality. Pertinently, this can be, to a large extent, managed by adopting control on the instability of slope and identification of the high-risk zones for land sliding to zero-in on the most vulnerable locations. This calls for assessment of all causative and triggering factors put the elements at risk which is, perhaps, the mainstay of the study. To achieve this goal, logistic regression model was employed taking cues both from satellite and ancillary field-based data. The output intriguingly coincides with the government reports by and large. This study identified the high, moderate and low risk zones in terms of landslide hazard, which can be utilized for priority mapping revealing the potentially fatal proximity of high-risk areas to the roads. It is, thus, recommended to restrict and if not, at least then suitably optimize the settlement expansion in areas at high risk, as, the Kalimpong-I block in Darjeeling district is one of the main hubs for administrative and commercial activities, it is naturally - prone to population growth and settlement expansion which demands taking up immediate actions by putting preventive remedial measures and simultaneous control of the indiscriminate settlement expansion. The study highlights the use of geospatial technology immensely helping for a better and more effective disaster management. Declarations Acknowledgement: We are thankful to LANDSAT team, USGS for providing us optical satellite imagery and also SRTM team, NASA for providing us the digital elevation Model for conducting this study. We pay our gratitude to the government departments of Darjeeling district and Kalimpong-I block administration for providing various secondary data for the entire study. Funding: No funding is involved in this work. Conflict of Interest: The authors declare that there is no conflict of interest in this work. Author's Contribution: Chalantika Laha Salui: 50% (Technical works & manuscript preparation) Shovanlal Chattoraj: 25% (Technical works & manuscript preparation) Prof. Ganga Prasad Prasain: 20% (inception & Manuscripts preparation) Prof. Poonam Sharma: 5% (Manuscripts preparation) References Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria Arab J Geosci. https://doi.org/10.1007/s12517-017-2980-6 Bai S, Lü G, Wang J, Zhou P, Ding L (2010) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62(1):139–149 Das I, Sahoo S, VanWesten C, Stein A, Hack R (2010) Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114(4):627–637 Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51:241–256 Ercanoglu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64:949–964 Feizizadeh B, Blaschke T, Nazmfar H (2014) GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. Int J Digit Earth 7:688–708 Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci. 18: 2161–2181. https://doi.org/10.5194/nhess-18-2161-2018 , 2018 Ghosh AMN (1950) Observation of the landslides of the 11th and 12th June 1950 in the Darjiling Himalayas. Reports, Geological Survey of India, India Ghosh S, Van Westen CJ, Carranza EJM, Jetten VG, Cardinali M (2011) Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Eng Geol 128:49–62 Kalantar B, Ueda N, Al-Najjar HAH, Saeidi V, Gibril MBA, Halin AA (2020) A comparison between three conditioning factors dataset for landslide prediction in the Sajadrood catchment of Iran. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3: 625–632 Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41 Liu Y, Dai L, Xiong H (2015) Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models. J Environ Plan Manag 58(6):1113–1136 Marjanović M, Kovačević M, Bajat B, Voženílek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225–234 Nandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11–20 Nautiyal SP, Darjeeling (1951) W.B. Unpublished Report, Geological survey of India, India Nefeslioglu HA, Duman TY, Durmaz S (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94:401–418 Oh HJ, Lee S (2011) Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environ Earth Sci 62(5):935–951 Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas. USA Eng Geol 69(3–4):331–343 Pradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301–320 Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187 Ren P, Gan S, Yuan X, Zong H, Xie X (2013) Spatial Expansion and Sprawl Quantitative Analysis of Mountain City Built-Up Area. In: Bian F, Xie Y, Cui X, Zeng Y (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science. Springer, Berlin, Heidelberg, pp 166–176 Salem M, Tsurusaki N, Divigalpitiya P (2019) Analyzing the Driving Factors Causing Urban Expansion in the Peri-Urban Areas Using Logistic Regression: A Case Study of the Greater Cairo Region. https://doi.org/10.3390/infrastructures4010004 . Infrastructures Sarkar A, Chauhan P (2020) Modeling spatial determinants of urban expansion of Siliguri a metropolitan city of India using logistic regression. J Adv Model Earth Syst. https://doi.org/10.1007/s40808-020-00815-9 Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep 5:1–15 Wang Z, Lu C (2018) Urban land expansion and its driving factors of mountain cities in China during 1990–2015. J Geogr Sci 28:1152–1166. https://doi.org/10.1007/s11442-018-1547-0 Yesilnacar E, Topal T (2005) Landslide Susceptibility Mapping: A Comparison of Logistic Regression and Neural Networks Methods in a Medium Scale Study, Hendek Region (Turkey). Eng Geol 79:251–266 Cite Share Download PDF Status: Published Journal Publication published 28 May, 2025 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 26 Feb, 2024 Reviewers invited by journal 17 Feb, 2024 Editor assigned by journal 02 Dec, 2023 First submitted to journal 01 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3676394","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273524895,"identity":"ee175666-714d-4b0a-bc8d-44e5b03fba3f","order_by":0,"name":"Chalantika Laha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACCQbGA0CKR4KBufEBiMFHhBYGoBYDoBbGZgOQFjZitYBsa5MACRDUwj+7+cGBnzv+yEi2H2yr/JpjJ8PGwPzw0Q18ltw5ZnCw94wBjzRPYttt2W3JQIexGRvn4NFiIJFgcIC3zYBHjgGoRXIbM1ALD5s0fi3pHw7+BWnhf9hWLLmtnhgtOQaHQbZISyS2MX7cdpiwFokbOQWHZduMeSRnPGyWZtx2nIeNmYBf+Gekb3z4tk3OXuJ88sGPP7dV2/OzNz98jE8LCmDmAZPEKgcBxh+kqB4Fo2AUjIIRAwBS6EUP5QTw/QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1644-6221","institution":"Indian Institute of Engineering Science and Technology,Shibpur","correspondingAuthor":true,"prefix":"","firstName":"Chalantika","middleName":"","lastName":"Laha","suffix":""},{"id":273524896,"identity":"e0def09e-2950-4d5e-96ec-894afa94f83a","order_by":1,"name":"Shovanlal Chattoraj","email":"","orcid":"","institution":"IIRS: Indian Institute of Remote Sensing","correspondingAuthor":false,"prefix":"","firstName":"Shovanlal","middleName":"","lastName":"Chattoraj","suffix":""},{"id":273524897,"identity":"ee638836-c293-47b0-a5c6-dce1584c3a41","order_by":2,"name":"Ganga Prasad Prasain","email":"","orcid":"","institution":"Tripura University","correspondingAuthor":false,"prefix":"","firstName":"Ganga","middleName":"Prasad","lastName":"Prasain","suffix":""},{"id":273524898,"identity":"3b9b438b-9757-43f4-ab1c-24b6eb9cdf7d","order_by":3,"name":"Poonam Sharma","email":"","orcid":"","institution":"Shaheed Bhagat Singh College","correspondingAuthor":false,"prefix":"","firstName":"Poonam","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2023-11-28 12:11:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3676394/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3676394/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-025-07324-x","type":"published","date":"2025-05-28T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51452210,"identity":"552a76a2-deed-44f0-a5e0-a536d22e1217","added_by":"auto","created_at":"2024-02-21 21:45:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102533,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/d935de6e5d6d084b2524a212.jpg"},{"id":51452209,"identity":"44b09ff1-154e-437f-9cfe-d61df7f04684","added_by":"auto","created_at":"2024-02-21 21:45:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228645,"visible":true,"origin":"","legend":"\u003cp\u003eCategorization of various landslide influencing factors\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/46e5c5893dfdba7ce3541586.jpg"},{"id":51452212,"identity":"72f31df0-6bb7-47db-99af-eb509fbdff9f","added_by":"auto","created_at":"2024-02-21 21:45:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184739,"visible":true,"origin":"","legend":"\u003cp\u003eFactors controlling the expansion of Settlement\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/555091d2ec06807a03585c07.jpg"},{"id":51452213,"identity":"9ec8e29a-7d51-4083-82e8-42824e1e8d94","added_by":"auto","created_at":"2024-02-21 21:45:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142892,"visible":true,"origin":"","legend":"\u003cp\u003eFinal Land slide Susceptibility zoning based on Logit (P) equation with Standardized Coefficients and ROC Curve of logistic regression generated for land slide prediction and the accuracy assessments of landslide susceptibility analysis\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/334544ac6838e351db404b10.jpg"},{"id":51452216,"identity":"cc52c81b-ebb5-4bcc-8107-216e854d939b","added_by":"auto","created_at":"2024-02-21 21:45:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117178,"visible":true,"origin":"","legend":"\u003cp\u003eAccuracy assessment of logit based urban expansion model and the Standardized Coefficients and ROC Curve of logistic regression generated for settlement expansion prediction\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/26752351fb13a3ae34cbada5.jpg"},{"id":51452214,"identity":"cfc01a84-29ee-46cc-8b31-8da469fe8bbc","added_by":"auto","created_at":"2024-02-21 21:45:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":259625,"visible":true,"origin":"","legend":"\u003cp\u003eProbability zoning for landslide occurrences and settlement expansion\u003c/p\u003e\n\u003cp\u003eAnd resultantHigh settlement probability zones coming under different land slide sensitive zones\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/a7029f7b70a39321e917e4b1.jpg"},{"id":51452211,"identity":"5e37b253-5491-4bcd-a63c-a5613f82c0c8","added_by":"auto","created_at":"2024-02-21 21:45:48","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":213814,"visible":true,"origin":"","legend":"\u003cp\u003eAreas highly suitable for settlement expansion within predicted very low land slide sensitive zone\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/89ed712a78882c81d8ca3c4b.jpg"},{"id":83782843,"identity":"88b7d1f3-1cf0-4d98-ac74-466bd47ee502","added_by":"auto","created_at":"2025-06-02 16:07:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1924965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3676394/v1/245f7999-2404-48cd-93ec-959da6486d36.pdf"}],"financialInterests":"","formattedTitle":"Risk Resilience of Growing Settlements in Landslide Prone Hilly Areas: Case Study on Kalimpong-I Block, Darjeeling District, West Bengal","fulltext":[{"header":"1. INTRODUCTON","content":"\u003cp\u003eLandslide is a large-scale landmass instability, triggered by slope imbalance and reduced resistance. Every year, it causes not only the collapse of hundreds of houses and other infrastructures but also the depletion of natural resources and lives. Eventually, it escalates into a natural disaster which causes devastation in significant amount and overwhelms the capability of people to adapt and avoid. The majority of this kind of natural disasters take place in rocky terrain. The high earthquake prone zone primarily encompasses these landslide-prone regions in the Himalayan topography (GSI report 2014). Froude and Petley, (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlighted the Indian Himalaya is one of the largest landslip hotspots and accounts for 16% of the world's rainfall-induced landslides.\u003c/p\u003e \u003cp\u003eThe majority of slope instability is caused by human activity, including quarrying, construction, and illegal hill-cutting etc. Researchers of University of Sheffield, England, reported that country-wise the most construction triggered land slide events occur in India i.e. 28% (Froude and Petley \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to GSI report (2014), Darjeeling Himalaya within the territory of West Bengal experienced 52 landslides only in 2014, whereas a total of 841 landslides occurred during 2000\u0026ndash;2014 (GSI Report 2014). GSI (2016) reported 87 landslides on 1st July, 2015. Darjeeling Himalaya is characterized by complex geological, geomorphological, and seismo-tectonic configuration. The Darjeeling region's hill ranges are extremely rough, structurally controlled, and constantly undergoing highly active denudation processes. The Darjeeling and Sikkim Himalayas' mountainous regions are part of the Himalayas in terms of geology of the Himalayan Fold-Thrust Belt (FTB) in motion. These complicacies are what cause the instability of the ground.\u003c/p\u003e \u003cp\u003eThe hilly settlement areas of Darjeeling Himalayan region have been growing fast for rapid population growth. Unplanned settlement expansion triggers the damage of lives due to landslides. Hence, a plan to restrict settlement expansion in landslide prone areas will be effective to prevent loss of human resources. For the purpose, the prediction of landslide occurrences i.e. landslide susceptibility analysis is necessary based on their influencing factors. Along with this, prediction on future settlement expansion based on favorable criteria also should be conducted, so that the preventive planning can be chalked out. Remote sensing plays an important play a part in creating a map of landslides and other maps with a thematic focus on landslip occurrences (Kalantar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). An integrated approach showcasing the landslide hazard vis-\u0026agrave;-vis settlement from an urban growth perspective is relatively novel idea (Ghosh et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe spatial decision making in GIS platform involves the layering of different landslide-influencing factor layers with their weighted contribution or contribution ratio to arrive at a final conclusion. There are various methodologies all over the world to calculate weightage of individual influencing factors. Some of them are based on real occurrence data whereas some are not. Among these, most commonly used are Analytical Hierarchical Process (AHP), Frequency Ratio, Information Value, different statistical prediction models like Support Vector Machine (SVM), Artificial Neural Network (ANN) etc. Simulations based on the real occurrence data are more acceptable because of its testing and training-based algorithm. Logistic regression is one such extensively used statistical prediction model (Ercanoglu and Temiz \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lee and Pradhan \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bai et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Oh and Lee \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pradhan, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ohlmacher and Davis \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) where, The entire computation is based on the trend line equation, which is produced by the correlation between organized causal components and actual occurrences in a binary response pattern. Based on the parametric values in the training sites, probability is estimated here using regression trend line analysis followed by the identification of various probable zones for future occurrences.\u003c/p\u003e \u003cp\u003eHence, this logistic regression model was implemented to retrieve the contribution of each triggering factor for predicting the future landslide prone areas (Nandi and Shakoor \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Duman et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nefeslioglu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pradhan \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) as well as the future settlement expansion (Salem et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sarkar and Chauhan \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e. Most of the studies involving landslide susceptibility analysis considered NDVI, Lithology, soil, slope, aspect, distance to fault, distance to road, distance to drainage, elevation, TWI, distance to ridges, precipitation, elevation, etc., but some researchers used fault density, road density, or drainage density instead of using the approach as spatial buffering (Shahabi \u0026amp; Hashim \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Feizizadeh et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e and Achour et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While Yesilnacar \u0026amp; Topal (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) took into account plan curvature and profile curvature, Marjanovi et al. (2011) focused on slope length rather than slope angle. Inputs included the surface area ratio, the stream power index (SPI), and sub-watershed basins. In addition to other common parameters, Regmi et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) also considered flow length, flow accumulation, tangential curvature, and solar radiation. The factors used as input affect the model's performance as well. Numerous studies used various influencing parameters behind the expansion of settlements too. As this study is focused on the hilly terrain, the factors triggering the settlement expansion should be very much terrain specific. The major factors taken by the researchers are slope, aspect, Drainage density, land use, Proximity to facilities like transport, water supply, power, commercial and administrative services, etc. (Wang and Lu \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ren et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Settlements tend to develop near the tourist viewpoints for income generation. Hence, the simulation for future settlement expansion based on such criteria can help to identify the vulnerable areas and preventive planning can be adopted.\u003c/p\u003e \u003cp\u003eThis kind of analysis is best suited for Kalimpong region which is very sensitive in terms of landslides (Ghosh \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Nautiyal \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1951\u003c/span\u003e). Every year lots of land slide occurrences take place resulting losses of lives. WBDMD considers it under severely critical zone for land slide disasters. At the same time, this is the area where rapid rate of population growth and urbanization was documented (74,746 population in 2011). Population within the municipal area of Kalimpong increases from 28,885 in 1981, to 38,832 in 1991 followed by 42,998 in 2001 and 49,403 in 2011. Hence, Kalimpong region is very much suitable for such analysis and Kalimpong-I block was taken for this study (Figure-1) as this block contains the urban center of the region. This analysis can help the concerned authority to mark the zone of restrictions for settlement built-up as well as to identify the spots for disaster mitigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Data Preparation and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data preparation for landslide Susceptibility Analysis:\u003c/h2\u003e \u003cp\u003eThe analysis of landslides' susceptibility is an overlay analysis based on different elements that affect the research region. In this study, influencing elements such land slope, relative relief, proximity to a road, land use and cover, lithological setup, lineament density, Normalized Differential Vegetation Index (NDVI), drainage density, soil, topographic wetness index (TWI), and aspect have been taken into account. In order to create these thematic geographical data sets, a digital elevation model with a spatial resolution of 20 m was created using spot heights and contours from the Survey of India's topographical maps of the area. Landsat-8 OLI optical satellite data was used. Secondary information was gathered from numerous sources, such as roads, soil, and lithology (Figure-2). The landslip inventory map was utilized to verify the results. Slope angle was derived from DEM as it\u0026rsquo;s a very important factor behind land slide. In the study area slope angle reaches up to 78\u0026deg;. Relative relief is also important factor as land slide occurs mostly in rugged terrain. It was calculated by interpolating the range of elevation data though IDW interpolation method. It ranges within the values 19 to 748 calculated for this study area. Road construction disturbs the slope stability and so distance from road impacts on the slope stability. Here, distances taken for 100m, 300m, 500m, 1km and 2km. Kalimpong-I block consists of land use / cover classes like Scrub, agriculture, agricultural plantation, Built-up, forest, forest plantation and river. Scrub, agricultural, and plantation land is extremely prone to subsidence. The rock groups found in the study region include Granite Gneiss, Quartinery Sediments, Gondwana, Daling, Siwalik, and Sikkim. Lineaments are the stratigraphy's geologically weak zones. Therefore, the likelihood of a landslip is higher where there are more lineaments. Here, the density was determined by the number of lines present. Landsat-8 OLI was used to calculate NDVI. The likelihood of a land slip diminishes as NDVI increases. The calculated NDVI of Kalimpong-I is from \u0026minus;\u0026thinsp;0.106235 to 0.515668. The calculated NDVI of Kalimpong-I is from \u0026minus;\u0026thinsp;0.106235 to 0.515668. Higher the drainage density, higher is the slope instability and soil moisture. Four types of soils are seen here. Most part of the block is covered by W002 type of soil having Siwalik and Daling group as lithological origin. These are coarse loamy soils that are overly drained and relatively shallow. Other varieties include very deep, poorly drained, coarse loamy soils (W008), moderately shallow (W004), and shallow, gravelly loamy soils (W001). Another element is the Topographic Wetness Index (TWI), which is calculated by multiplying the slope by the flow accumulation in that particular pixel. The slope failure is encouraged by the index that indicates how wet the surface is. Maximum TWI calculated for this block is 18.32. Besides all these, rainfall and tectonic activities like earthquake play very important role in land sliding. Here, rainfall was not taken in the list of factors as the study area is a very small area in terms of the available rainfall data. Variation will not be there to show its influence. Earthquake is a sudden occurrence and was not considered directly as earthquake prone zones. Rather, Lineaments were considered here which indirectly explain the tectonic activeness of the area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data preparation for settlement expansion Analysis:\u003c/h2\u003e \u003cp\u003eSettlement expansion in the hilly areas depends more on the physical factors than cultural. Hence for the probability of a non-settlement cell to convert to settlement cell depends on certain criteria of that cell which are slope, aspect, drainage density, land use etc. physical factors as well as association factors like Distance to road, Accessibility to tourist viewpoints, water supply, urban center etc. As settlement expansion is restricted in protected and restricted forest (as per government forest boundary) and rivers, those areas are excluded from the simulation database. Distances from road, facilities like water supply, urban center, and tourist attractions were calculated as Euclidian distances which gives us clear picture about the vulnerability of the elements which are at stake due to landslide hazard. Then they were classified to prioritize individual subclass (Figure-3). Table-1 is showing these hierarchical subclasses taken for the logistic regression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHierarchical order of sub-classes of individual influencing factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from road (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccessibility to water supply\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance to Tourist attractions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDistance to Urban Centers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrainage Density\u003c/p\u003e \u003cp\u003e(DD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLand use / Land Cover\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;17200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45\u0026ndash;78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2117\u0026ndash;3298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3424\u0026ndash;5120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4346\u0026ndash;5762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11761\u0026ndash;17200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3501\u0026ndash;4800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eForest Plantation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1466\u0026ndash;2116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1939\u0026ndash;3423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3501\u0026ndash;4345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7641\u0026ndash;11760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2401\u0026ndash;3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u0026ndash;32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAgricultural Plantation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e978\u0026ndash;1465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e636\u0026ndash;1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2831\u0026ndash;3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4450\u0026ndash;7640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1500\u0026ndash;2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eScrubland\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e571\u0026ndash;977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2079\u0026ndash;2830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;4450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245\u0026ndash;570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1290\u0026ndash;2078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Methodology (Logistic Regression):\u003c/h2\u003e \u003cp\u003eUsing this method, numbers between 0 and 1 are used to calculate the likelihood of occurrence. (Wang and Sassa 2005, Ayalew and Yamagishi 2005). The regression trend line equation, which is created based on the correlation between independent causative factors and the dependent occurrences that come from those causes, provides the foundation for the entire calculation. As a result, the model's main goal is to find the equation that best captures the relationship between the dependent variable and a set of independent parameters. (Guzzetti et al., 1999, Rowbotham and Dudycha, 1998, Tolga et al. 2005). The event's absence or presence serves as the dependent variable (y) in this analysis. The classes of the thematic data layers serve as the independent variables in this investigation. influencing either landslide occurrences or settlement expansion. The dependent variable is of binary type (occurrence data), with values of 1 (presence) or 0 (absence). If p defines the likelihood that a pixel will slide, then: p\u0026thinsp;=\u0026thinsp;1 / 1\u0026thinsp;+\u0026thinsp;e-z\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ewhere, Z is a value from \u0026minus;\u0026infin; to +\u0026infin;, determined by the following equation;\u003c/p\u003e\u003cp\u003eZ\u0026thinsp;=\u0026thinsp;B0\u0026thinsp;+\u0026thinsp;B1X1\u0026thinsp;+\u0026thinsp;B2X2 +. .. + BnXn\u003c/p\u003e\u003cp\u003eb0 is the model's intercept, n the number of causal factors, b their relative weights, and x the conditional factors.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003eThis study analyses the future risk for the settlements which are to be built up at the fringe of existing settlement as because of their unawareness of the probability of land sliding in that area. For the purpose, first, logistic regression prediction model was implemented for the landslide susceptibility modeling. The logit equation was generated based on 11 independent variables (Figure-3) which were taken as the influencing factors for land sliding in the hilly area. The dependent variable was taken from the landslide inventory data or training set pixels. The response variable was formatted in binary format i.e. 1 for occurrence cell and 0 for non-occurrence cell. Each response cell is having 11 independent variable data.\u003c/p\u003e \u003cp\u003eBased on the trend analysis of these variables for training pixels, the probability was retrieved for rest of the cell by logit (P) equation.\u003c/p\u003e \u003cp\u003eAs a result, the output of Logit (P) or Pred (RESPONSE) for each pixel is:\u003c/p\u003e \u003cp\u003ePred (RESPONSE)\u0026thinsp;=\u0026thinsp;1 / (1\u0026thinsp;+\u0026thinsp;exp (-(-15.1280969395951\u0026thinsp;+\u0026thinsp;10.130509342953 * TWI\u0026thinsp;+\u0026thinsp;0.922165778006754 * SOIL\u0026thinsp;+\u0026thinsp;0.582049096236194 * RRELIEF\u0026thinsp;+\u0026thinsp;0.906193820336884 * RD_PROX\u0026thinsp;+\u0026thinsp;14.7223593884136 * NDVI-1.12864977549746 * LUSE\u0026thinsp;+\u0026thinsp;0.915154011933381 * LINMT_DENS\u0026thinsp;+\u0026thinsp;0.924366423539928 * LITHOLOGY\u0026thinsp;+\u0026thinsp;8.70632609652196 * D_DENS\u0026thinsp;+\u0026thinsp;12.2723316927091 * ASPECT\u0026thinsp;+\u0026thinsp;1.77835765300871 * SLOPE)))\u003c/p\u003e \u003cp\u003eStandardized Coefficients can be seen for the individual factor with a confidence interval of 95%. Area under ROC curve is 0.917 which shows the model to be highly accurate (Figure-4).\u003c/p\u003e \u003cp\u003eHence, with this high accuracy logit equation, the value for each cell as the probability to slide was calculated. This was followed by the classification of total range of probability into five susceptibility zones. It can be seen that most of the land slide occurrence cells are coming under high to very high susceptible zones. This assures the reliability of the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnother prediction was made for the future settlement expansion in this area. The research tried to build up such an awareness plan by predicting the high landslide prone areas and to coincide them with the future built up areas. As settlements are restricted to expand in forest boundaries and rivers, the area to be considered for prediction modeling excluded the areas under forest (as per govt. forest boundary) and rivers. As, only the expansion is to be granted, the exclusion of present settlement was also done.\u003c/p\u003e \u003cp\u003eFor the prediction modeling of future settlement expansion, as there is nothing like inventory data as such, so, for the validation and accuracy assessment of the model 1999 to 2011 databases was formulated as training dataset. Independent variables were taken as Slope, aspect, Drainage density, land use / land cover, accessibility to road, water supply, tourist attractions, urban centre for this area, whereas the expanded settlements in 2011 were taken as the positive response (1) variable and not expanded cells in the 1999 settlement fringe areas were taken as negative response (0) variable for generating the logit equation.\u003c/p\u003e \u003cp\u003ePred(RESPONSE)\u0026thinsp;=\u0026thinsp;1 / (1\u0026thinsp;+\u0026thinsp;exp(-(3.03280731789662\u0026ndash;0.355741688657557 * LUSE-0.399724562258824 * SLOPE\u0026thinsp;+\u0026thinsp;0.0114867641861869 * ASPECT\u0026thinsp;+\u0026thinsp;0.155361539091004 * DD\u0026thinsp;+\u0026thinsp;0.347824531963753 * RDDIST \u0026minus;\u0026thinsp;0.115459986977181 * TOURSPOT \u0026minus;\u0026thinsp;0.460248380960188 * URBNCNTR \u0026minus;\u0026thinsp;0.244915394031267 * EUCLWS)))\u003c/p\u003e \u003cp\u003eTo validate the model, this equation was implemented on 2011 data to get future probability values for each cell. The output zones were verified with the real expanded settlement in 2019. The accuracy achieved was 79.22% as this was the percentage of real occurrence cells coming under high probability zone (Figure- 5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith this accuracy, the logit equation was implemented on 2019 image to get future settlement expansion. For further analysis as per the objective of the study, the land slide susceptibility zoning raster also was subset by the forest-river mask. The range of probability values for the masked study area is from 0.000182158 to 0.978251 for landslide risk zoning and from 0.0390989 to 0.974798 for settlement expansion probability. Hence probability raster was classified into 3 prominent zones: low, moderate and high for both of the prediction modeling.\u003c/p\u003e \u003cp\u003eAs the analysis is based on zoning, the strategy behind the zone classification is very much important. In case of land slide susceptibility zonation, if the whole range of probability value is divided equally, a high frequency of data is coming under the higher range zone and the analysis will not be scientific. That\u0026rsquo;s why; classifications were made based on defined probability percentage values to keep both land slide and settlement expansion probability raster into a common and neutral platform.\u003c/p\u003e \u003cp\u003eTo make the analysis more relevant, classification was made manually to highlight the sensitive zone with probability 80% as high probable zone. The moderate probability was taken for the range of 50\u0026ndash;80% and below 50% was taken as low probable zone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnd resultant High settlement probability zones coming under different land slide sensitive zones\u003c/p\u003e \u003cp\u003eFigure- 6 is showing the base input datasets for the main analysis of this study. These probability raster for Kalimpong-I block can help the planners to make preventive as well as mitigation panning by overlay analysis. Based on probability values in Figure-6, the major sensitive areas can be identified and are enlisted in Table-2.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable-2\u003c/strong\u003e \u003cp\u003eAreas with High Level of probability of land sliding as an output of Logistic Regression\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillage wise Distribution of pixels with probability more than 80%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVillage wise Distribution of pixels with probability more than 90%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthern Bhalukhope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorthern Bhalukhope (along the road near Bhalukhope forest)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthern Kalimpong adjacent to Dansong forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Sindibon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouthern Sindibon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePudung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWest Pudung\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYokprintam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorth Yokprintam (along the right bank of Rilli river)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern Icha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIcha near brder with Kalimpong-II\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeokbhir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeokbhir (along major road to Lulagaon)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanralbong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingi (all over the village)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthern and Western Lulagaon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern, Eastern and Western Samether\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorth-west Samether (along the major road)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthern and Eastern Suruk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorthern half of Suruk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern and Eastern Nimbong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNimbong (along the road to Samether forest)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParinger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouthern Paringer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNobgaon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNobgaon (all over the village)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSothern Lish catchment area forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth and Eastern Yang Makum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKaffir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthern Pemling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFew patches in western Lulagaon forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParts of Riyong forest and Birik forest near Riyong Railway station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlong NH-10 near Riyong Railway station\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the report by West Bengal Disaster Management Division in 2017 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.wbdmd.gov.in/writereaddata/uploaded/DP/Disaster%20Management%20Plan%20of%20KALIMPONG.pdf\u003c/span\u003e\u003cspan address=\"http://www.wbdmd.gov.in/writereaddata/uploaded/DP/Disaster%20Management%20Plan%20of%20KALIMPONG.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a list of vulnerable areas was given. There, major gram panchayets with a greater number of vulnerable locations are given as: Bhalukhope, Bong, Kalimpong Khasmahal, Upper Iche, Yangmakum, Teesta, Sandepong, Pabringtar, Samether, Kaffer, Samalbong, Seokbir etc. Hence, the outcomes of this study are matching with their report except few. Teesta is not that much sensitive in the output map of this study. On the other hand, Pemling, Nobgaon, Suruk, portions of Lulagaon, Singi, Pudung was identified as prominent sensitive areas. Even, parts of Riyong forest and Birik forest near Riyong Railway station, NH-10 adjacent areas also were highlighted as sensitive areas. Moreover, the scenario in the output raster by Logistic Regression matches to a great extent with the government report. Hence, the process is reliable for such study. On 5th August, 2020, a major landslide occurred at Bhalukhope damaging the major road to Kalimpong town. In July, 2015, there were few events on NH-10 and places within Kalimpong town like 6 mile, 14 mile, and Pedong. Up to 38 people died overall, while numerous others went missing. Roads were cut off, and properties were lost. NH 10 was also got damaged resulting the cut in communication between Kalimpong and Lava. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.longdom.org/open-access/causes-of-landslides-in-darjeeling-himalayas-during-junejuly-2015-2167-0587-1000173.pdf\u003c/span\u003e\u003cspan address=\"https://www.longdom.org/open-access/causes-of-landslides-in-darjeeling-himalayas-during-junejuly-2015-2167-0587-1000173.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) A major landslide took place on NH-10 at a place called 29 mile in July, 2019. In Figure- 6, these areas are within the high-risk zone as an output of Logistic regression. Most of the land slide events affected roads and, in many cases, houses were lost. Therefore, with such risk analysis, the habitats under risk can be made aware. No more settlement encroachment in those areas should be allowed.\u003c/p\u003e \u003cp\u003eFor the purpose, the areas for further settlement expansion were simulated by the same model as of the landslide prediction i.e., logistic regression. From Figure-6, it can be identified that, settlement expansion has high probability (\u0026gt;\u0026thinsp;80%) in villages like Pudung, Kanke bong, Slokbhir, Lulageon, Singi, Samether, Nimbong, Nobgaon to a larger extent. whereas, Paringer, Yang Makum, Southern lowlands of Mangpong forest has the probability\u0026thinsp;\u0026gt;\u0026thinsp;80% is few parcels. Figure-7 shows the predicted settlement cells which are coming under the high, moderate and low land slide susceptible zones. The concerned authority should restrict the development of built-ups in the highly sensitive areas first.\u003c/p\u003e \u003cp\u003eSettlements which are tend to be emerged randomly along the minor roads connecting small habitations in the central parts of the district should be panned with utmost priority. Major areas to specially look after are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLowlands, south of Mangpong forest.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eS-E and Northern part of Yang Makkum village.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEastern half of Nobgaon Khasmahal.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRandomly distributed parcels in Nimbong and Paringar Khasmahal.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSouthern part of Lish catchment area.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNorthern part of Suruk.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWest and South of Samether.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigh concentration in Singi.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWestern part of Lulagaon.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEastern part of Slokbhir.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSo, these are the major areas where probability of settlement built-up as well as land sliding, both are high and should be taken care of.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure-7 is showing the areas with a high sensitivity to land sliding within the municipal area of Kalimpong. The population in this area is growing very rapidly. In 2001, 63.5% of the block was staying in the municipal area where in 2011 it increased to 66% with a decadal variation of 14.9% during this period. Hence, the population density is ever increasing within this confined area. As in Figure: 6, it can be seen that there is a very less scope for urban expansion within this municipal area; so, the trend must be the vertical expansion. Therefore, the development of multi-storied buildings started to accommodate population growth which obviously exerts extra stress to the slope. The designing of the buildings should be kept under constant observation in the high-risk areas. SH-12 is at high risk. So, planning and management should be properly undertaken in this high population density area.\u003c/p\u003e \u003cp\u003eOn the other hand, Figure-6 is showing the safe and favorable areas for settlement expansion. Here, cells with landslide probability of less than 50% and settlement expansion probability more than 75% were overlaid. The common areas are the safe areas for settlement expansion and can be encouraged for mitigation purpose.\u003c/p\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eLandslide is the main alarming hazard in Darjeeling-Sikkim Himalayas, annually triggered by tropical rainfall leading to sudden loss of wealth, houses and even causality. Pertinently, this can be, to a large extent, managed by adopting control on the instability of slope and identification of the high-risk zones for land sliding to zero-in on the most vulnerable locations. This calls for assessment of all causative and triggering factors put the elements at risk which is, perhaps, the mainstay of the study. To achieve this goal, logistic regression model was employed taking cues both from satellite and ancillary field-based data. The output intriguingly coincides with the government reports by and large. This study identified the high, moderate and low risk zones in terms of landslide hazard, which can be utilized for priority mapping revealing the potentially fatal proximity of high-risk areas to the roads. It is, thus, recommended to restrict and if not, at least then suitably optimize the settlement expansion in areas at high risk, as, the Kalimpong-I block in Darjeeling district is one of the main hubs for administrative and commercial activities, it is naturally - prone to population growth and settlement expansion which demands taking up immediate actions by putting preventive remedial measures and simultaneous control of the indiscriminate settlement expansion. The study highlights the use of geospatial technology immensely helping for a better and more effective disaster management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to LANDSAT team, USGS for providing us optical satellite imagery and also SRTM team, NASA for providing us the digital elevation Model for conducting this study. We pay our gratitude to the government departments of Darjeeling district and Kalimpong-I block administration for providing various secondary data for the entire study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding is involved in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest: \u0026nbsp;\u003c/strong\u003eThe authors declare that there is no conflict of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s Contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChalantika Laha Salui: \u0026nbsp;50% (Technical works \u0026amp; manuscript preparation)\u003c/p\u003e\n\u003cp\u003eShovanlal Chattoraj: 25% (Technical works \u0026amp; manuscript preparation)\u003c/p\u003e\n\u003cp\u003eProf. Ganga Prasad Prasain: 20% (inception \u0026amp; Manuscripts preparation)\u003c/p\u003e\n\u003cp\u003eProf. Poonam Sharma: 5% (Manuscripts preparation)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAchour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria Arab J Geosci. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12517-017-2980-6\u003c/span\u003e\u003cspan address=\"10.1007/s12517-017-2980-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai S, L\u0026uuml; G, Wang J, Zhou P, Ding L (2010) GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environ Earth Sci 62(1):139\u0026ndash;149\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas I, Sahoo S, VanWesten C, Stein A, Hack R (2010) Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114(4):627\u0026ndash;637\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51:241\u0026ndash;256\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErcanoglu M, Temiz FA (2011) Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu, Turkey). Environ Earth Sci 64:949\u0026ndash;964\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeizizadeh B, Blaschke T, Nazmfar H (2014) GIS-based ordered weighted averaging and Dempster\u0026ndash;Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. Int J Digit Earth 7:688\u0026ndash;708\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFroude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat. Hazards Earth Syst. Sci. 18: 2161\u0026ndash;2181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/nhess-18-2161-2018\u003c/span\u003e\u003cspan address=\"10.5194/nhess-18-2161-2018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 2018\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh AMN (1950) Observation of the landslides of the 11th and 12th June 1950 in the Darjiling Himalayas. Reports, Geological Survey of India, India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh S, Van Westen CJ, Carranza EJM, Jetten VG, Cardinali M (2011) Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Eng Geol 128:49\u0026ndash;62\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalantar B, Ueda N, Al-Najjar HAH, Saeidi V, Gibril MBA, Halin AA (2020) A comparison between three conditioning factors dataset for landslide prediction in the Sajadrood catchment of Iran. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3: 625\u0026ndash;632\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33\u0026ndash;41\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Dai L, Xiong H (2015) Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models. J Environ Plan Manag 58(6):1113\u0026ndash;1136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarjanović M, Kovačević M, Bajat B, Vožen\u0026iacute;lek V (2011) Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geol 123(3):225\u0026ndash;234\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110:11\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNautiyal SP, Darjeeling (1951) W.B. Unpublished Report, Geological survey of India, India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNefeslioglu HA, Duman TY, Durmaz S (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94:401\u0026ndash;418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOh HJ, Lee S (2011) Landslide susceptibility mapping on Panaon Island, Philippines using a geographic information system. Environ Earth Sci 62(5):935\u0026ndash;951\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas. USA Eng Geol 69(3\u0026ndash;4):331\u0026ndash;343\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePradhan B (2010) Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens 38(2):301\u0026ndash;320\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172\u0026ndash;187\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen P, Gan S, Yuan X, Zong H, Xie X (2013) Spatial Expansion and Sprawl Quantitative Analysis of Mountain City Built-Up Area. In: Bian F, Xie Y, Cui X, Zeng Y (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science. Springer, Berlin, Heidelberg, pp 166\u0026ndash;176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalem M, Tsurusaki N, Divigalpitiya P (2019) Analyzing the Driving Factors Causing Urban Expansion in the Peri-Urban Areas Using Logistic Regression: A Case Study of the Greater Cairo Region. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/infrastructures4010004\u003c/span\u003e\u003cspan address=\"10.3390/infrastructures4010004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Infrastructures\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarkar A, Chauhan P (2020) Modeling spatial determinants of urban expansion of Siliguri a metropolitan city of India using logistic regression. J Adv Model Earth Syst. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-020-00815-9\u003c/span\u003e\u003cspan address=\"10.1007/s40808-020-00815-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment. Sci Rep 5:1\u0026ndash;15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Lu C (2018) Urban land expansion and its driving factors of mountain cities in China during 1990\u0026ndash;2015. J Geogr Sci 28:1152\u0026ndash;1166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11442-018-1547-0\u003c/span\u003e\u003cspan address=\"10.1007/s11442-018-1547-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYesilnacar E, Topal T (2005) Landslide Susceptibility Mapping: A Comparison of Logistic Regression and Neural Networks Methods in a Medium Scale Study, Hendek Region (Turkey). Eng Geol 79:251\u0026ndash;266\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"landslide susceptibility analysis, settlement expansion, vulnerability assessment, risk resilience","lastPublishedDoi":"10.21203/rs.3.rs-3676394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3676394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDarjeeling-Sikkim Himalaya is a hotspot of landslide occurrences in India. Losses of natural and human resources has become common and frequent news for this area as an effect of landslide. At the same time, it\u0026rsquo;s a very potential zone from developmental and tourism perspective which leads to emerging population growth and settlement expansion. The directional magnitude of this sprawling depends on the physical, environmental and infrastructural strengths of the area. But this can be threatened by landslide. Hence, to minimize loss of lives and property, optimization and restriction of developmental activities in highly sensitive areas is the need of the hour.\u003c/p\u003e \u003cp\u003eKalimpong is a highly sensitive site for such issue for its emerging urban agglomeration. Hence, the case study was conducted in Kalimpong-I block in Darjeeling District. Quantitative simulation by multivariate logistic regression was carried out based on influencing factors and landslide inventory data for landslide susceptibility analysis. Digital elevation model (DEM), Landsat-8 OLI satellite imagery and also some secondary data were used to generate the individual spatial database to formulate dependent variables. Spatial overlay analysis with the final outputs for predicted urban sprawling and predicted landslide occurrence zones enabled the managing authority to identify future highly vulnerable zones as well as the safer zones for settlement and infrastructure expansion. This helped the authority to restrict the set-ups resulting minimization of elements at risk. It can help in the disaster preparedness as well as mitigation planning. Therefore, this study shows a holistic approach towards effective disaster management and risk resilience.\u003c/p\u003e","manuscriptTitle":"Risk Resilience of Growing Settlements in Landslide Prone Hilly Areas: Case Study on Kalimpong-I Block, Darjeeling District, West Bengal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 21:45:41","doi":"10.21203/rs.3.rs-3676394/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-26T10:36:54+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-17T16:38:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-02T09:47:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2023-12-02T03:00:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9852f7a6-3383-4fca-8629-4465dd9cd208","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:00:45+00:00","versionOfRecord":{"articleIdentity":"rs-3676394","link":"https://doi.org/10.1007/s11069-025-07324-x","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2025-05-28 15:57:21","publishedOnDateReadable":"May 28th, 2025"},"versionCreatedAt":"2024-02-21 21:45:41","video":"","vorDoi":"10.1007/s11069-025-07324-x","vorDoiUrl":"https://doi.org/10.1007/s11069-025-07324-x","workflowStages":[]},"version":"v1","identity":"rs-3676394","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3676394","identity":"rs-3676394","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}