Assessing Landslide Risk Probability in the Garhwal Himalayas, India Using a GIS-Based Bivariate Statistical Approach

Assessing Landslide Risk Probability in the Garhwal Himalayas, India Using a GIS-Based Bivariate Statistical Approach | 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 Assessing Landslide Risk Probability in the Garhwal Himalayas, India Using a GIS-Based Bivariate Statistical Approach Harjeet Kaur, Shubham Badola, Ravinder Singh, Surya Parkash This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4575738/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Landslides is deadliest disasters which occur frequently without warning causing damages and human causalities in the vulnerable areas. The topography of the region affects the frequency of landslides occurrences, as well as the impact of outside factors including intense rain, seismic activity, changes in groundwater levels, snowmelt, stream erosion, flooding, or any combination of these natural events. The research study investigates the risk probability of Garhwal Himalaya with the help of several causative factors, including slope, aspect, curvature, elevation, proximity to river, proximity to road, rainfall, lineament density, NDVI, NDBI and census data of 2011. Landslide inventory was prepared and classified into training data (70%) and testing data (30%). The landslide risk probability and susceptibility for the area of interest have been obtained using the frequency ratio (FR) approach. The resultant susceptibility and risk probability maps were classified into five i.e very low, low, medium, high, very high. The study reveals that 15.8% of the areas fall under the very high susceptibility zone, while 17.3% area in the very high risk zone. Further, the receiver operating characteristic-area under the curve (ROC-AUC) was used to calculate the landslide risk probability map's overall model accuracy, that turned up to 75.25%. The findings can be used further by planners and relevant authorities for landslip mitigation and control. Frequency Ratio Landslide susceptibility map Risk Probability ROC-AUC Garhwal Himalaya Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Landslides are one of the deadliest and most frequent dangers in hilly areas. They frequently occur without warning and cause damage and human casualties. The local topography and the influence of geological and geomorphological processes determine the frequency of landslides. However, external forces including excessive rainfall, earthquakes, flooding, snow melting, stream erosion, changes in ground water level, or any combination of these natural phenomena can cause landslides on unstable slopes [ 1 – 5 ]. Previous research has also shown that human activity, including the built-up areas, deforestation, forest-fire, shifting agriculture, and shoddy road construction, increases the frequency and magnitude of landslides in many hilly or mountainous areas [ 6 – 10 ]. Therefore, reducing the likelihood of landslide occurrences on unstable slopes and evaluating the short- and long-term harmful effects of landslides on the natural landscape require an interdisciplinary approach. The Himalaya, western Ghats, and the eastern Ghats are among the hilly regions of India that are most vulnerable to landslides, accounting for over 12.6% of the country's land area [ 11 ]. Landslides commonly occur in the youngest mountain chain of the world i.e. the Indian Himalayan Region (IHR) as a result of internal elements such surface drainage groundwater, high relief, steep slopes, higher altitude, unstable soil, and lithology setting. But prior research also highlights that landslides were further exacerbated in many potentially unstable slope areas by external factors like intense rain, earthquakes, and human activities like deforestation, shifting agriculture, road construction, and agricultural expansion [ 12 – 16 ]. The landslide susceptibility map is a useful technique for managing the risk of landslides. Under the presumption that landslides would continue to occur under the same circumstances as they have in the past, these maps can be produced using spatial prediction of landslides [ 17 ]. Thus, landslide susceptibility can be evaluated by examining the spatial relationship between a group of causative factors and previous landslide incidents. Recently, a large number of landslide susceptibility maps have been developed in various parts of the world using Geographic Information Systems (GIS). Landslide vulnerability is heightened by a combination of factors such as geological conditions, land use patterns, and climate change impacts, posing significant risks to communities worldwide (UNDRR, 2019; Guzzetti et al., 2020). Understanding these dynamics in vulnerable regions is crucial for effective risk mitigation strategies and risk assessment. Currently, the most often used method for assessing landslide vulnerability is the statistical technique, which is a subjective method. This strategy has been used in a variety of techniques, including logistic regression [ 20 ], weights of evidence [ 19 ], the information value method [ 23 ], frequency ratio [ 18 ]. The frequency ratio (FR) approach is one of these techniques that is most frequently used for assessing landslide vulnerability and has good performance [ 17 ]. The present work aims to create a landslide susceptibility and risk probability map for Garhwal Himalaya, Uttarakhand, India through FR model. The performance of the FR model was evaluated using the ROC-AUC curve. The study on landslide risk probability assessment is very limited in the Garhwal Himalaya. Therefore present research work is carried out using Normalized Difference Built-up Index (NDBI) and census data of 2011 (recent data of 2021 is awaited). 2. Study Area Located in the Western Himalaya region, the Garhwal region (Fig. 1 ) is administratively a part of the northern Indian state of Uttarakhand. The region, known as the Garhwal Mandal Administration Commissionerate, occupies 32486 Km 2 (60.67%) of the state of Uttarakhand, whereas the remaining 21,035 Km 2 (39.33%) is occupied by the Kumaun Mandal Administration Commissionerate. It is divided into seven districts: Dehradun, Pauri, Uttarkashi, Tehri, Uttarkashi, Chamoli, Haridwar, and Rudraprayag. The Garhwal Himalaya stretches from 29 o 31’9” N – 31 o 26’5” N latitude to 77 o 33’5” E – 80 o 6’0” E longitude, covering an area of about 29,089 km 2 [ 34 ]. Ranging from 500 meters above sea level to over 7,000 meters above sea level. The Great Himalaya features magnificent peaks like Nanda Devi, Kamet, Trishul, Chaukhamba, Kedarnath, Bunderpunch, Gangotri, Yamunotri, and Badrinath, among others. Its width is between 40 and 50 kilometres, and its relief ranges from 3000 to 6000 metres. This area exhibits unique geographical features together with extremely diverse ecological and geological conditions. There are several thrusts and faults throughout the Garhwal Himalaya, which have controlled the river drainage network in addition to limiting the geological identities. Additionally, from north to south, these thrusts and faults divide the Trans, Greater, Lesser, and Shiwalik Himalayan regions [ 35 ]. Average temperature ranges from − 10°C to 36°C, and precipitation ranges from 900 mm to 2500 mm. It comprises of following physiography types – lower Himalaya/Shivalik ranges, middle Himalaya and the great Himalaya ranges. During summer, the average temperature varies from 26.5 o C (highest) to 7. 5 o C (lowest). By contrast, the temperature goes down to -8 o C in the Tungnath and − 3 o C in the Badrinath pilgrimages. At the same time, few areas witnesses temperatures as high as 45 o C. Average annual rainfall ranges from 925mm to 2,220mm[ 34 ]. Flash floods, landslides, Earthquake, forest fire, Landslide Lake Outburst Flood (LLOF) and Glacial Lake Outburst Flood (GLOF) are the key hazards in these locations. 3. Material and Methodology Landslide susceptibility mapping using the statistical methods like frequency ratio method involves a systematic analysis of various contributing factors. This approach requires the collection and integration of spatial data related to landslide occurrences and the influential environmental variables such as slope, geology, soil type, and rainfall. The methodology used in the current study is show in figure below (Fig. 2 ): 3.1 Causative Factors Understanding risk circumstances and mechanisms that have caused and controlled previous landslides in the area of interest is essential to estimating the likelihood of future landslides [ 23 , 25 ]. The likelihood of landslide occurrences can be determined by analyzing particular causative factors in conjunction with a landslide inventory. In this study, this was achieved by integrating 10 types of data related to topography, meteorology, and existing landslide records. Using MS-Excel and the ArcGIS environment, thematic layers of causative factors for landslide incidents have been created, based on a review of the literature [ 25 , 26 ]. This paper provides a comprehensive examination of the frequency ratio method for generating a landslide susceptibility map and then evaluates the associated risk by considering population density and the Normalized Differential Built-up Index (NDBI). NDBI is a satellite-based metric that helps identify urban areas, providing insights into human-induced changes in land use. When used in landslide risk estimation, it can indicate where increased urbanization might elevate the potential for landslide occurrences due to soil instability and altered water drainage patterns [ 24 ]. There are no established guidelines for identifying the parameters or key contributing factors that lead to landslides. Because of this, scientists studying landslide susceptibility zonation and prediction across the globe [ 23 , 27 , 28 ] have considered various causative factors dependent on the physiography and data availability of the study region. As result, landslide susceptibility and risk probability map have been prepared using the causative factors (based on availability of data) in the research area. The causative factors taken into account are given in table below (Table 1 ): Table 1 Causative factors responsible for susceptibility and risk of landslide and their data source. S. No Causative Factor Data Source 1 Slope SRTM DEM (30 m) 2 Aspect 3 Curvature 4 Elevation 5 Rainfall Annual average (2004 to 2023) IMD (30 m) 6 Soil depth Clay, Coarse fragment, sand, silt (5 to 15 m) SoilGrid 7 Proximity to road (km) (only major roads) Diva GIS 8 Proximity to river (km) SRTM DEM (30m) 9 Lineament Density Bhukosh 10 NDVI Landsat 9 11* NDBI Landsat 9 12* Census 2011 (gridded estimates of India population at a resolution of 1 kilometer) NASA SEDAC * Factor for risk probability map Based on the orientation of the slope face, aspect was separated into nine directions (i.e., Flat, N, NE, E, SE, S, SW, W, and NW). Three directions were identified for curvature: flat, positive (convex orientation), and negative (concave orientation). Based on the type of vegetation, the NDVI was divided into five categories (Fig. 3 ). Slope map was classified into five classes using 15° intervals. Using the natural breaks method (Jenks), the causative factors of soil depth, rainfall, elevation, proximity to lineament, proximity to roads, and proximity to rivers were each divided into five distinct categories. 3.2 Landslide Inventory The Bhukosh portal of the Geological Survey of India provided the landslide inventory, which was then split into training data (70%) and testing data (30%). Following that, the training data set was rasterized and overlaying each factor above the other to create a tabular area for each factor. After calculating frequency ratio indices, prediction rates for each of the different layers were determined. 3.3 Frequency Ratio (FR) : FR is a form of bi-variate statistical technique that evaluates a landslide's susceptibility based on the location of the landslide and each parameter that contributed to the landslide. According to Lee (2005), FR determines the geographical association between landslide causative factors and landslide site. Frequency ratio indexing was constructed based on the association between landslide events and sub-classes of each causative factors. The landslide susceptibility index is then calculated by adding together each frequency ratio index. The following equation (Pham et al., 2015) is used to generate FR values: $$FR = \left(\frac{\frac{Nslpix}{Ntslpix}}{{\frac{Ncpix}{Ntcpix}}_{}}\right)$$ 1 ................................................. Where FR = frequency ratio, Nslpix = landslide pixel/area in given landslide factor class, Ntslpix = total area of a landslide in the entire study area, Ncpix = area of the class in the study area, Ntcpix = total pixel area in the entire study area. 3.4 Accuracy assessment To assess the accuracy of causative factors on landslide occurrence a threshold-independent method i.e., Receiver Operating Characteristics (ROC) is implemented by presenting the results of the accuracy values attained against a fixed value [ 29 ]. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) are key metrics for validating landslide risk maps, providing insights into the accuracy and predictive power of models for landslide susceptibility [ 30 ]. The ROC curve aids in evaluating a model's capacity to distinguish between regions that are prone to landslides and those that are not by graphically representing the relationship between sensitivity (true positive rate) and specificity (false positive rate) at various thresholds. The AUC, derived from the ROC curve, gives a single value to indicate the model's overall performance; an AUC of 0.5 suggests random prediction, while an AUC of 1.0 indicates perfect accuracy. For landslide risk map validation, the AUC allows researchers to gauge how well the model aligns with actual landslide occurrences, typically using a landslide inventory for comparison [ 31 , 32 ]. Higher AUC values suggest better model reliability, which is crucial for informed decisions in land use planning, disaster management, and community safety in landslide-prone regions. A risk map was created by superimposing the landslide susceptibility map with the NBDI (30 m) and 1 km (resampled to 30 m) population density on top of the rasterized Cencus data (2011). 4. Result and Discussion Assessing the relative significance of different causative factors involves evaluating the extent to which each factor influences the likelihood of landslide occurrences (Fig. 3 ). After reclassifying each causative factor, the normalized index for each sub-class was determined using frequency ratio algorithms (Table 2 ) and landslide susceptibility map was prepared, and classified into 5 classes i.e. Very High, High, Medium, Low and Very Low. As per the FR analysis (Table 2 ) Proximity to river shows higher predictor rate followed by proximity to lineament, elevation, aspect, slope, rainfall, proximity to road and soil. However, NDVI and curvature shows low predictor rate. Table 2 Calculation of indices for predictor rate using frequency ratio method for the causative factors after reclassifying them as mentioned above in the text. Parameter Class Class Pixel Landslide Pixel FR RF PR Aspect Flat 193185 90 0.000465875 0.056907952 3.385969642 N 4545229 5149 0.001132836 0.138379249 NE 4378785 4014 0.000916693 0.111976682 E 4411653 3653 0.000828034 0.101146806 SE 4569063 3643 0.000797319 0.097394823 S 4964612 3946 0.000794825 0.097090249 SW 4917313 4577 0.000930793 0.113699068 W 4627447 5328 0.001151391 0.140645749 NW 4123405 4819 0.001168694 0.142759422 Total 36730692 35219 0.00818646 Curvature Concave 3785915 4400 0.001162203 0.383654888 1.915188998 flat 15152063 13677 0.000902649 0.297973744 Convex 17792714 17160 0.00096444 0.318371368 Total 36730692 35237 0.003029292 Elevation 1494 8582427 2651 0.000308887 0.062654767 3.516364074 2832 10775996 12865 0.001193857 0.242162494 4170 6633591 9442 0.001423362 0.288715312 5508 4716289 6437 0.001364844 0.276845591 7100 6012204 3842 0.000639034 0.129621835 Total 36720507 35237 0.004929984 Proximity to Lineament (km) 550 565071 14 2.47756E-05 0.029128589 Total 36803833 35314 0.003002384 NDVI Snow -1 to -0.1 7551 0.000850561 0.166964012 1 Water -0.1 to 0 3508 0.001143817 0.224529666 Built-up 0 to 0.1 9106 0.001055988 0.207289039 Vegetation 0.1 to 0.5 9122 0.000876412 0.172038468 Forest 0.5 to 1 5934 0.001167501 0.229178815 Total 36058796 35221 0.005094279 Rainfall (mm) 711–941 4070415 2334 0.000573406 0.139339148 2.667881733 941–1171 5651743 4342 0.000768259 0.18668886 1171–1401 11859927 15114 0.001274375 0.309676601 1401–1631 11412438 12215 0.001070323 0.260091422 1631–1859 2873012 1232 0.000428818 0.104203968 Total 35867535 35237 0.004115182 Proximity to River (km) 550 2534061 367 0.000144827 0.037254348 Total 36885992 35171 0.003887515 Proximity to Road (km) 400 756219 192 0.000253895 0.067352372 Total 36885992 35171 0.003769648 Slope 0–18 7286543 2533 0.000347627 0.070118709 3.294882089 18–36 8011669 7621 0.000951238 0.191870946 36–54 9749828 10579 0.001085045 0.218860767 54–72 8366336 9888 0.001181879 0.238392951 72–90 3316317 4616 0.001391906 0.280756627 Total 36730693 35237 0.004957694 Soil (cm) 14 12647669 9662 0.000763935 0.197481911 Total 36616227 35237 0.003868381 4.1 Landslide Risk Map According to Biswakarma, P., et. al. 2023 NDBI and Census data were used to prepare risk map. The primary qualitative risk was prepared as per the available data with respect to NDBI and Census data. Landslide Risk Probability (LRP) = (LSM + NDBI + Census) / 3 The weight of each causative factor was calculated based on cumulative frequency probability of the factors with respect to LSM produced (Fig. 4 ). It is seen that proximity to river has highest weightages followed by rainfall (mainly during monsoon) and there after followed by elevation, aspect, slope, reflecting the anthropogenic intervention for slope failure. Curvature and soil depth have medium influence on the LSM. NDVI is observed to be the least due to presence of snow fed region and forest area in large part but it cannot be exempted as it gives us the information about the healthy vegetation that can act as a good indicator for natural slope stability. By considering the pixel values of both, i.e., the parameters and the landslide locations, the indices values of the spatial link between the two are derived. Using a raster calculator, all the thematic layers were combined to create the final landslide susceptibility map (Fig. 5 (a)), after the computation of indices for each sub-class. In addition, LSM was divided into five groups according to how vulnerable the area is to landslide based on natural break. LSM was overlaid with layer of NDBI and Census data (2011) for knowing the probability of the risk (Fig. 5 (b)), in the area of interest. A few locations had extremely high risk because of cloud cover in higher reaches of the Himalaya (same can be seen in NDBI layer), and the plain south-west region of Haridwar also had high risk at some point because of the dense population. Based on the pixel count of each division provided in Table 3 given below, the percentage was computed to determine the area covered by each of the five divisions of vulnerability and risk of the region Table 3 Percentage of the pixels falling into 5 different categories of landslide susceptibility and Risk probability for Garhwal Uttarakhand Calculation using FR OBJECTID Index Class Pixel (LSM) % LSM Class Pixel (Risk) % RISK 1 Very Low 2911936 8.154768588 2612736 7.316870103 2 Low 7215456 20.20661647 3815367 10.68479354 3 Medium 9304883 26.05797916 10488330 29.37217852 4 High 10614292 29.72493042 12610294 35.31465987 5 Very High 5661816 15.85570537 6181656 17.31149798 From the table, it is clear that the areas with high and medium susceptibility are more prone to landslides, as indicated by the selected causative factors. However, a larger proportion of the study area is classified as low-susceptibility, likely because it encompasses extensive river networks, where toe erosion leads to smaller-scale landslides. Regions adjacent to rivers or streams, roads, or areas with significant human activity fall into the medium to very high-risk categories. On the other hand, low and very low-risk zones generally consist of regions with dense vegetation or forest cover, lower slopes, and locations distant from rivers and anthropogenic constructions. It's also worth noting that the higher elevations in the Himalayan terrain are typically snow-covered, where mass movements might occur as debris flows due to snowmelt or, at times, as avalanches or Glacial Lake Outburst Floods (GLOFs). Without the model's validation, maps that are subject to landslides cannot be regarded as relevant or trustworthy [ 22 ]. Validating the predictive model is therefore a crucial stage. By superimposing 70% of landslide locations over the contributing components, final landslide susceptibility maps were created. As validation data, 30% of the landslide sites were chosen at random. There are several methods for validating LSMs. It is discovered that evaluating the models' performance with basic metrics is a subpar approach. The Receiver Operating characteristic-Area Under Curve (ROC-AUC) approach has gained popularity recently as a way to organize and visualize prediction model performance and is frequently used in research to gauge a prediction model's accuracy [ 23 ]. In the study ROC was plotted and AUC value obtained was 75.25% (Fig. 6 ). Conclusion The threat of landslides looming over mountainous terrains, posing significant risks to life and property. Both natural forces and anthropogenic activities contribute to increase in landslide susceptibility of regions in the Himalayan region. The FR results indicate that the likelihood of landslides is greater in areas nearer to the river, with significant rainfall, and along lineaments, followed by factors such as elevation, aspect, and slope. LSM shows that the study area has medium to high susceptibility area falling for landslide susceptibility. The district of Rudraprayag, adjoining areas of Joshimath, Nainital, Mussoorie etc (Fig. 5 -b) falls under high to very high risk probability. Through application of interdisciplinary approaches and sophisticated methods like the frequency ratio model, landslide susceptibility map offered crucial insights into susceptibility mapping and risk probability for Garhwal region of Uttarakhand. Validation of the risk probability map is paramount for ensuring reliability, with metrics like ROC-AUC which provided valuable indicators of accuracy of 75.25% for the risk probability of the landslide susceptibility using FR. By leveraging comprehensive analysis and validation processes, we can develop robust strategies to safeguard lives and infrastructure in vulnerable regions, mitigating the devastating impacts of landslides on communities and ecosystems. Urgent actions are required to implement effective landslide hazard management and mitigation strategies, in areas of Uttarakhand Garhwal Himalaya, where diverse ecological, geological and climatic conditions may increase landslide risk. Declarations Funding There was no financial support for this research. Author Contributions Statement Shubham Badola and Harjeet Kaur developed the methodology, generated the results, and also contributed to writing the manuscript. Overall GIS modelling is done by Shubham Badola. Ravinder Singh and Surya Parkash provided expert guidance in selecting the causative factors for the study and offered overall guidance for writing the paper. All authors read and approved the final manuscript. 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International journal of environmental research and public health, 17(12), 4206. https://doi.org/10.3390/ijerph17124206 Singh, P., Sur, U., Rai, P. K., & Singh, S. K. (2023). Landslide susceptibility prediction using frequency ratio model: a case study of Uttarakhand, Himalaya (India). Proceedings of the Indian National Science Academy, 89(3), 600-612. https://doi.org/10.1007/s43538-023-00171-z Sonker, I., & Tripathi, J. N. (2022). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio method in Sikkim Himalaya. Quaternary Science Advances, 8, 100067. https://doi.org/10.1016/j.qsa.2022.100067 Biswakarma, P., Joshi, V., Abdo, H. G., Almohamad, H., Abdullah Al Dughairi, A., & Al-Mutiry, M. (2023). An integrated quantitative and qualitative approach for landslide susceptibility mapping in West Sikkim district, Indian Himalaya. Geomatics, Natural Hazards and Risk, 14(1). https://doi.org/10.1080/19475705.2023.2273781 Sati, V. P. (2015). Climate change and socio-ecological transformation in high mountains: an empirical study of Garhwal Himalaya. Change and Adaptation in Socio-Ecological Systems, 2(1). https://doi.org/10.1515/cass-2015-0005 Chaudhary, S., Kumar, A., & Negi, M. (2019). A geospatial appraisal of Garhwal Himalayan bio-geodiversity and its ecotourism potentials. Int J Res Anal Rev, 6(1), 911-926. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-4575738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329611986,"identity":"6cceb010-3340-4d87-9e46-eb1dd4319b7e","order_by":0,"name":"Harjeet Kaur","email":"","orcid":"","institution":"Technical Officer, VHS-CDC","correspondingAuthor":false,"prefix":"","firstName":"Harjeet","middleName":"","lastName":"Kaur","suffix":""},{"id":329611989,"identity":"61e9e9bc-8436-4755-b391-6c23110ef8bd","order_by":1,"name":"Shubham Badola","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACCQY2ZhBtAEIPGGyATMbGA8RrSWBIA2lpIEnLYbAgXi3ys3vMHhfUbGMwZz+88UFCzXm7te2HgbbU2ETj0mJw54y58Yxjtxkse9KKDRKO3U7ediYRqOVYWm4DLi0SOWbSPGy3GQwO5JhJJLDdTjY7ANTC2HAYpxb5GSAt/4Bazr8Bavl3Ltns/EP8WhhuALXwtgG1ABkSiW0H7MxuELDF4EZauTFv320eyxnPig0S+5ITzG4AbUnA4xf5GcnbHvN8uy1nzp+88cGHb3b2ZufTHz74UGOD22FQwANjJIJVJhBQjgLsSVE8CkbBKBgFIwMAAOdVY2zaZOI0AAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Disaster Management (NIDM)","correspondingAuthor":true,"prefix":"","firstName":"Shubham","middleName":"","lastName":"Badola","suffix":""},{"id":329611993,"identity":"9f5704da-659c-4521-9420-3f99ad2500c0","order_by":2,"name":"Ravinder Singh","email":"","orcid":"","institution":"Former Senior Policy Specialist, Ministry of External Affairs (MEA)","correspondingAuthor":false,"prefix":"","firstName":"Ravinder","middleName":"","lastName":"Singh","suffix":""},{"id":329611996,"identity":"66e85d11-7535-4a4b-8734-a4bb5d09954d","order_by":3,"name":"Surya Parkash","email":"","orcid":"","institution":"National Institute of Disaster Management (NIDM)","correspondingAuthor":false,"prefix":"","firstName":"Surya","middleName":"","lastName":"Parkash","suffix":""}],"badges":[],"createdAt":"2024-06-13 11:07:14","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4575738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4575738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60891101,"identity":"53143752-46ee-4089-9717-1efc565ac27c","added_by":"auto","created_at":"2024-07-23 08:59:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1144884,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the Study area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4575738/v1/547f93e506fa4a6aa1da7b19.png"},{"id":60891100,"identity":"6bf38b2b-3db4-4f75-a732-3b89a5d1749f","added_by":"auto","created_at":"2024-07-23 08:59:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153351,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological flow chart used in the study\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4575738/v1/124c786f24dbc9409a667272.png"},{"id":60891106,"identity":"0ec5e7cc-cee7-4257-a964-a955379b949a","added_by":"auto","created_at":"2024-07-23 08:59:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3135305,"visible":true,"origin":"","legend":"\u003cp\u003eLandslide conditioning factors taken into study (a)Aspect, (b)Curvature, (c)Elevation, (d)Lineament Density, (e) NDVI, (f)Proximity to river, (g)Proximity to road, (h)Rainfall, (i)Slope, (j)Soil depth, (k)Census and (l)NDBI\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4575738/v1/7741a92ff6fb24fe9c8b45a2.png"},{"id":60891104,"identity":"9359d324-4b02-4ef7-b918-0b2095544c6a","added_by":"auto","created_at":"2024-07-23 08:59:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25019,"visible":true,"origin":"","legend":"\u003cp\u003eWeightage of Individual Factor susceptible to landslide\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4575738/v1/de417eba2cf68f1b4f386642.png"},{"id":60891105,"identity":"489885b9-3da9-4bff-81fa-883d812ac4f7","added_by":"auto","created_at":"2024-07-23 08:59:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":683146,"visible":true,"origin":"","legend":"\u003cp\u003eLandslide susceptible map (a) and Risk map (b) of Garhwal, Uttarakhand\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4575738/v1/e651da57e7da6d23f53d7d67.png"},{"id":60891102,"identity":"faeab124-33bc-46e6-9718-c4311d2ccb9b","added_by":"auto","created_at":"2024-07-23 08:59:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17927,"visible":true,"origin":"","legend":"\u003cp\u003eROC-AUC for the Risk map using proposed method.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4575738/v1/62e2550425690454c384b604.png"},{"id":66977620,"identity":"499532a1-b90b-4777-b974-ab0b77f233ec","added_by":"auto","created_at":"2024-10-18 16:31:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6878378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4575738/v1/67516814-0bba-4a29-afae-ce51787a3b3a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Landslide Risk Probability in the Garhwal Himalayas, India Using a GIS-Based Bivariate Statistical Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLandslides are one of the deadliest and most frequent dangers in hilly areas. They frequently occur without warning and cause damage and human casualties. The local topography and the influence of geological and geomorphological processes determine the frequency of landslides. However, external forces including excessive rainfall, earthquakes, flooding, snow melting, stream erosion, changes in ground water level, or any combination of these natural phenomena can cause landslides on unstable slopes [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous research has also shown that human activity, including the built-up areas, deforestation, forest-fire, shifting agriculture, and shoddy road construction, increases the frequency and magnitude of landslides in many hilly or mountainous areas [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, reducing the likelihood of landslide occurrences on unstable slopes and evaluating the short- and long-term harmful effects of landslides on the natural landscape require an interdisciplinary approach.\u003c/p\u003e \u003cp\u003eThe Himalaya, western Ghats, and the eastern Ghats are among the hilly regions of India that are most vulnerable to landslides, accounting for over 12.6% of the country's land area [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Landslides commonly occur in the youngest mountain chain of the world i.e. the Indian Himalayan Region (IHR) as a result of internal elements such surface drainage groundwater, high relief, steep slopes, higher altitude, unstable soil, and lithology setting. But prior research also highlights that landslides were further exacerbated in many potentially unstable slope areas by external factors like intense rain, earthquakes, and human activities like deforestation, shifting agriculture, road construction, and agricultural expansion [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe landslide susceptibility map is a useful technique for managing the risk of landslides. Under the presumption that landslides would continue to occur under the same circumstances as they have in the past, these maps can be produced using spatial prediction of landslides [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, landslide susceptibility can be evaluated by examining the spatial relationship between a group of causative factors and previous landslide incidents. Recently, a large number of landslide susceptibility maps have been developed in various parts of the world using Geographic Information Systems (GIS). Landslide vulnerability is heightened by a combination of factors such as geological conditions, land use patterns, and climate change impacts, posing significant risks to communities worldwide (UNDRR, 2019; Guzzetti et al., 2020). Understanding these dynamics in vulnerable regions is crucial for effective risk mitigation strategies and risk assessment. Currently, the most often used method for assessing landslide vulnerability is the statistical technique, which is a subjective method. This strategy has been used in a variety of techniques, including logistic regression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], weights of evidence [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the information value method [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], frequency ratio [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The frequency ratio (FR) approach is one of these techniques that is most frequently used for assessing landslide vulnerability and has good performance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present work aims to create a landslide susceptibility and risk probability map for Garhwal Himalaya, Uttarakhand, India through FR model. The performance of the FR model was evaluated using the ROC-AUC curve.\u003c/p\u003e \u003cp\u003eThe study on landslide risk probability assessment is very limited in the Garhwal Himalaya. Therefore present research work is carried out using Normalized Difference Built-up Index (NDBI) and census data of 2011 (recent data of 2021 is awaited).\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eLocated in the Western Himalaya region, the Garhwal region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is administratively a part of the northern Indian state of Uttarakhand. The region, known as the Garhwal Mandal Administration Commissionerate, occupies 32486 Km\u003csup\u003e2\u003c/sup\u003e (60.67%) of the state of Uttarakhand, whereas the remaining 21,035 Km\u003csup\u003e2\u003c/sup\u003e (39.33%) is occupied by the Kumaun Mandal Administration Commissionerate. It is divided into seven districts: Dehradun, Pauri, Uttarkashi, Tehri, Uttarkashi, Chamoli, Haridwar, and Rudraprayag. The Garhwal Himalaya stretches from 29\u003csup\u003eo\u003c/sup\u003e31\u0026rsquo;9\u0026rdquo; N \u0026ndash; 31\u003csup\u003eo\u003c/sup\u003e26\u0026rsquo;5\u0026rdquo; N latitude to 77\u003csup\u003eo\u003c/sup\u003e33\u0026rsquo;5\u0026rdquo; E \u0026ndash; 80\u003csup\u003eo\u003c/sup\u003e6\u0026rsquo;0\u0026rdquo; E longitude, covering an area of about 29,089 km\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Ranging from 500 meters above sea level to over 7,000 meters above sea level. The Great Himalaya features magnificent peaks like Nanda Devi, Kamet, Trishul, Chaukhamba, Kedarnath, Bunderpunch, Gangotri, Yamunotri, and Badrinath, among others. Its width is between 40 and 50 kilometres, and its relief ranges from 3000 to 6000 metres. This area exhibits unique geographical features together with extremely diverse ecological and geological conditions. There are several thrusts and faults throughout the Garhwal Himalaya, which have controlled the river drainage network in addition to limiting the geological identities. Additionally, from north to south, these thrusts and faults divide the Trans, Greater, Lesser, and Shiwalik Himalayan regions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Average temperature ranges from \u0026minus;\u0026thinsp;10\u0026deg;C to 36\u0026deg;C, and precipitation ranges from 900 mm to 2500 mm.\u003c/p\u003e \u003cp\u003eIt comprises of following physiography types \u0026ndash; lower Himalaya/Shivalik ranges, middle Himalaya and the great Himalaya ranges. During summer, the average temperature varies from 26.5\u003csup\u003eo\u003c/sup\u003eC (highest) to 7. 5 \u003csup\u003eo\u003c/sup\u003eC (lowest). By contrast, the temperature goes down to -8\u003csup\u003eo\u003c/sup\u003eC in the Tungnath and \u0026minus;\u0026thinsp;3\u003csup\u003eo\u003c/sup\u003eC in the Badrinath pilgrimages. At the same time, few areas witnesses temperatures as high as 45\u003csup\u003eo\u003c/sup\u003eC. Average annual rainfall ranges from 925mm to 2,220mm[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Flash floods, landslides, Earthquake, forest fire, Landslide Lake Outburst Flood (LLOF) and Glacial Lake Outburst Flood (GLOF) are the key hazards in these locations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Material and Methodology","content":"\u003cp\u003eLandslide susceptibility mapping using the statistical methods like frequency ratio method involves a systematic analysis of various contributing factors. This approach requires the collection and integration of spatial data related to landslide occurrences and the influential environmental variables such as slope, geology, soil type, and rainfall. The methodology used in the current study is show in figure below (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Causative Factors\u003c/h2\u003e \u003cp\u003eUnderstanding risk circumstances and mechanisms that have caused and controlled previous landslides in the area of interest is essential to estimating the likelihood of future landslides [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The likelihood of landslide occurrences can be determined by analyzing particular causative factors in conjunction with a landslide inventory. In this study, this was achieved by integrating 10 types of data related to topography, meteorology, and existing landslide records. Using MS-Excel and the ArcGIS environment, thematic layers of causative factors for landslide incidents have been created, based on a review of the literature [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This paper provides a comprehensive examination of the frequency ratio method for generating a landslide susceptibility map and then evaluates the associated risk by considering population density and the Normalized Differential Built-up Index (NDBI). NDBI is a satellite-based metric that helps identify urban areas, providing insights into human-induced changes in land use. When used in landslide risk estimation, it can indicate where increased urbanization might elevate the potential for landslide occurrences due to soil instability and altered water drainage patterns [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are no established guidelines for identifying the parameters or key contributing factors that lead to landslides. Because of this, scientists studying landslide susceptibility zonation and prediction across the globe [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] have considered various causative factors dependent on the physiography and data availability of the study region. As result, landslide susceptibility and risk probability map have been prepared using the causative factors (based on availability of data) in the research area. The causative factors taken into account are given in table below (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\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\u003eCausative factors responsible for susceptibility and risk of landslide and their data source.\u003c/p\u003e \u003c/div\u003e \u003c/caption\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 \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCausative Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\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\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSRTM DEM (30 m)\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\u003eAspect\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\u003eCurvature\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\u003eElevation\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\u003eRainfall Annual average (2004 to 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIMD (30 m)\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\u003eSoil depth\u003c/p\u003e \u003cp\u003eClay, Coarse fragment, sand, silt (5 to 15 m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoilGrid\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\u003eProximity to road (km)\u003c/p\u003e \u003cp\u003e(only major roads)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiva GIS\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\u003eProximity to river (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRTM DEM (30m)\u003c/p\u003e \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\u003eLineament Density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBhukosh\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\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandsat 9\u003c/p\u003e \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\u003eNDBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandsat 9\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\u003eCensus 2011 (gridded estimates of India population at a resolution of 1 kilometer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNASA SEDAC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* Factor for risk probability map\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the orientation of the slope face, aspect was separated into nine directions (i.e., Flat, N, NE, E, SE, S, SW, W, and NW). Three directions were identified for curvature: flat, positive (convex orientation), and negative (concave orientation). Based on the type of vegetation, the NDVI was divided into five categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Slope map was classified into five classes using 15\u0026deg; intervals. Using the natural breaks method (Jenks), the causative factors of soil depth, rainfall, elevation, proximity to lineament, proximity to roads, and proximity to rivers were each divided into five distinct categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Landslide Inventory\u003c/h2\u003e \u003cp\u003eThe Bhukosh portal of the Geological Survey of India provided the landslide inventory, which was then split into training data (70%) and testing data (30%). Following that, the training data set was rasterized and overlaying each factor above the other to create a tabular area for each factor. After calculating frequency ratio indices, prediction rates for each of the different layers were determined.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Frequency Ratio (FR)\u003c/b\u003e: FR is a form of bi-variate statistical technique that evaluates a landslide's susceptibility based on the location of the landslide and each parameter that contributed to the landslide. According to Lee (2005), FR determines the geographical association between landslide causative factors and landslide site. Frequency ratio indexing was constructed based on the association between landslide events and sub-classes of each causative factors. The landslide susceptibility index is then calculated by adding together each frequency ratio index. The following equation (Pham et al., 2015) is used to generate FR values:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$FR = \\left(\\frac{\\frac{Nslpix}{Ntslpix}}{{\\frac{Ncpix}{Ntcpix}}_{}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e.................................................\u003c/p\u003e \u003cp\u003eWhere FR\u0026thinsp;=\u0026thinsp;frequency ratio,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eNslpix\u0026thinsp;=\u0026thinsp;landslide pixel/area in given landslide factor class,\u003c/p\u003e\u003cp\u003eNtslpix\u0026thinsp;=\u0026thinsp;total area of a landslide in the entire study area,\u003c/p\u003e\u003cp\u003eNcpix\u0026thinsp;=\u0026thinsp;area of the class in the study area,\u003c/p\u003e\u003cp\u003eNtcpix\u0026thinsp;=\u0026thinsp;total pixel area in the entire study area.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Accuracy assessment\u003c/h2\u003e \u003cp\u003eTo assess the accuracy of causative factors on landslide occurrence a threshold-independent method i.e., Receiver Operating Characteristics (ROC) is implemented by presenting the results of the accuracy values attained against a fixed value [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) are key metrics for validating landslide risk maps, providing insights into the accuracy and predictive power of models for landslide susceptibility [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The ROC curve aids in evaluating a model's capacity to distinguish between regions that are prone to landslides and those that are not by graphically representing the relationship between sensitivity (true positive rate) and specificity (false positive rate) at various thresholds. The AUC, derived from the ROC curve, gives a single value to indicate the model's overall performance; an AUC of 0.5 suggests random prediction, while an AUC of 1.0 indicates perfect accuracy. For landslide risk map validation, the AUC allows researchers to gauge how well the model aligns with actual landslide occurrences, typically using a landslide inventory for comparison [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Higher AUC values suggest better model reliability, which is crucial for informed decisions in land use planning, disaster management, and community safety in landslide-prone regions.\u003c/p\u003e \u003cp\u003eA risk map was created by superimposing the landslide susceptibility map with the NBDI (30 m) and 1 km (resampled to 30 m) population density on top of the rasterized Cencus data (2011).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result and Discussion","content":"\u003cp\u003eAssessing the relative significance of different causative factors involves evaluating the extent to which each factor influences the likelihood of landslide occurrences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After reclassifying each causative factor, the normalized index for each sub-class was determined using frequency ratio algorithms (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and landslide susceptibility map was prepared, and classified into 5 classes i.e. Very High, High, Medium, Low and Very Low.\u003c/p\u003e \u003cp\u003eAs per the FR analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) Proximity to river shows higher predictor rate followed by proximity to lineament, elevation, aspect, slope, rainfall, proximity to road and soil. However, NDVI and curvature shows low predictor rate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalculation of indices for predictor rate using frequency ratio method for the causative factors after reclassifying them as mentioned above in the text.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClass Pixel\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLandslide Pixel\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193185\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000465875\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056907952\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e3.385969642\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4545229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001132836\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.138379249\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4378785\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4014\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000916693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.111976682\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4411653\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3653\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000828034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101146806\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4569063\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3643\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000797319\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097394823\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4964612\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3946\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000794825\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097090249\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSW\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4917313\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4577\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000930793\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113699068\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4627447\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5328\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001151391\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.140645749\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNW\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4123405\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4819\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001168694\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.142759422\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36730692\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35219\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00818646\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCurvature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcave\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3785915\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001162203\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.383654888\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.915188998\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eflat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15152063\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13677\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000902649\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.297973744\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConvex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17792714\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17160\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00096444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.318371368\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36730692\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003029292\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1494\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8582427\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2651\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000308887\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062654767\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e3.516364074\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2832\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10775996\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12865\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001193857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.242162494\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4170\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6633591\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9442\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001423362\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.288715312\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5508\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4716289\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6437\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001364844\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.276845591\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6012204\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3842\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000639034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.129621835\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36720507\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004929984\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eProximity to Lineament (km)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; 100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18246839\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21532\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00118004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.393034384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e3.64506227\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100–250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11111049\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9819\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000883715\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.294337781\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250–400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5067207\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3569\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000704333\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2345912\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400–550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1813667\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00020952\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069784643\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e565071\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.47756E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.029128589\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36803833\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35314\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003002384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSnow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1 to -0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7551\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000850561\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166964012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1 to 0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3508\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001143817\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.224529666\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 to 0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001055988\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.207289039\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 to 0.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9122\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000876412\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.172038468\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 to 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5934\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001167501\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229178815\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36058796\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35221\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005094279\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e711–941\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4070415\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2334\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000573406\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.139339148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e2.667881733\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941–1171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5651743\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4342\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000768259\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18668886\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1171–1401\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11859927\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15114\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001274375\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.309676601\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1401–1631\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11412438\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12215\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001070323\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.260091422\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1631–1859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2873012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1232\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000428818\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.104203968\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35867535\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004115182\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eProximity to River (km)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; 100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10127069\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16344\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001613892\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.415147617\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e4.635160416\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100–250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9789108\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10090\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001030737\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.265140464\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250–400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8266509\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6290\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000760902\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.195729581\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400–550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6169245\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2080\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000337156\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08672799\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 550\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2534061\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000144827\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037254348\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36885992\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003887515\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eProximity to Road (km)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; 50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21161490\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21759\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001028236\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.272767071\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e2.443182129\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50–150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8189891\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8049\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000982797\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.260713222\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150–300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4261296\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3380\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000793186\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.210413815\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300–400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2517096\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1791\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000711534\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18875352\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e756219\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000253895\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067352372\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36885992\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003769648\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7286543\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2533\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000347627\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.070118709\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e3.294882089\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18–36\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8011669\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7621\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000951238\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.191870946\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36–54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9749828\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10579\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001085045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.218860767\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54–72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8366336\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9888\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001181879\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.238392951\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72–90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3316317\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4616\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001391906\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.280756627\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36730693\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004957694\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSoil (cm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; 5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2724687\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1307\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000479688\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124002302\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e2.120899506\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5–8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e296463\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000590293\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.152594306\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8–11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133699\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00088258\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.228152183\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11–14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20813709\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23975\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001151885\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.297769298\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12647669\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000763935\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.197481911\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36616227\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35237\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003868381\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Landslide Risk Map\u003c/h2\u003e \u003cp\u003eAccording to Biswakarma, P., et. al. 2023 NDBI and Census data were used to prepare risk map. The primary qualitative risk was prepared as per the available data with respect to NDBI and Census data.\u003c/p\u003e \u003cp\u003eLandslide Risk Probability (LRP) = (LSM + NDBI + Census) / 3\u003c/p\u003e \u003cp\u003eThe weight of each causative factor was calculated based on cumulative frequency probability of the factors with respect to LSM produced (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It is seen that proximity to river has highest weightages followed by rainfall (mainly during monsoon) and there after followed by elevation, aspect, slope, reflecting the anthropogenic intervention for slope failure. Curvature and soil depth have medium influence on the LSM. NDVI is observed to be the least due to presence of snow fed region and forest area in large part but it cannot be exempted as it gives us the information about the healthy vegetation that can act as a good indicator for natural slope stability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy considering the pixel values of both, i.e., the parameters and the landslide locations, the indices values of the spatial link between the two are derived. Using a raster calculator, all the thematic layers were combined to create the final landslide susceptibility map (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a)), after the computation of indices for each sub-class. In addition, LSM was divided into five groups according to how vulnerable the area is to landslide based on natural break. LSM was overlaid with layer of NDBI and Census data (2011) for knowing the probability of the risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (b)), in the area of interest. A few locations had extremely high risk because of cloud cover in higher reaches of the Himalaya (same can be seen in NDBI layer), and the plain south-west region of Haridwar also had high risk at some point because of the dense population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the pixel count of each division provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e given below, the percentage was computed to determine the area covered by each of the five divisions of vulnerability and risk of the region\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of the pixels falling into 5 different categories of landslide susceptibility and Risk probability for Garhwal Uttarakhand\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCalculation using FR\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOBJECTID\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClass Pixel (LSM)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% LSM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClass Pixel (Risk)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% RISK\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2911936\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.154768588\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2612736\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.316870103\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\u003eLow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7215456\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.20661647\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3815367\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.68479354\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\u003eMedium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9304883\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.05797916\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10488330\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.37217852\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\u003eHigh\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10614292\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.72493042\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12610294\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.31465987\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\u003eVery High\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5661816\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.85570537\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6181656\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.31149798\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFrom the table, it is clear that the areas with high and medium susceptibility are more prone to landslides, as indicated by the selected causative factors. However, a larger proportion of the study area is classified as low-susceptibility, likely because it encompasses extensive river networks, where toe erosion leads to smaller-scale landslides. Regions adjacent to rivers or streams, roads, or areas with significant human activity fall into the medium to very high-risk categories. On the other hand, low and very low-risk zones generally consist of regions with dense vegetation or forest cover, lower slopes, and locations distant from rivers and anthropogenic constructions. It's also worth noting that the higher elevations in the Himalayan terrain are typically snow-covered, where mass movements might occur as debris flows due to snowmelt or, at times, as avalanches or Glacial Lake Outburst Floods (GLOFs).\u003c/p\u003e \u003cp\u003eWithout the model's validation, maps that are subject to landslides cannot be regarded as relevant or trustworthy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Validating the predictive model is therefore a crucial stage. By superimposing 70% of landslide locations over the contributing components, final landslide susceptibility maps were created. As validation data, 30% of the landslide sites were chosen at random. There are several methods for validating LSMs. It is discovered that evaluating the models' performance with basic metrics is a subpar approach. The Receiver Operating characteristic-Area Under Curve (ROC-AUC) approach has gained popularity recently as a way to organize and visualize prediction model performance and is frequently used in research to gauge a prediction model's accuracy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the study ROC was plotted and AUC value obtained was 75.25% (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe threat of landslides looming over mountainous terrains, posing significant risks to life and property. Both natural forces and anthropogenic activities contribute to increase in landslide susceptibility of regions in the Himalayan region. The FR results indicate that the likelihood of landslides is greater in areas nearer to the river, with significant rainfall, and along lineaments, followed by factors such as elevation, aspect, and slope. LSM shows that the study area has medium to high susceptibility area falling for landslide susceptibility. The district of Rudraprayag, adjoining areas of Joshimath, Nainital, Mussoorie etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e-b) falls under high to very high risk probability. Through application of interdisciplinary approaches and sophisticated methods like the frequency ratio model, landslide susceptibility map offered crucial insights into susceptibility mapping and risk probability for Garhwal region of Uttarakhand. Validation of the risk probability map is paramount for ensuring reliability, with metrics like ROC-AUC which provided valuable indicators of accuracy of 75.25% for the risk probability of the landslide susceptibility using FR. By leveraging comprehensive analysis and validation processes, we can develop robust strategies to safeguard lives and infrastructure in vulnerable regions, mitigating the devastating impacts of landslides on communities and ecosystems. Urgent actions are required to implement effective landslide hazard management and mitigation strategies, in areas of Uttarakhand Garhwal Himalaya, where diverse ecological, geological and climatic conditions may increase landslide risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no financial support for this research.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShubham Badola and Harjeet Kaur developed the methodology, generated the results, and also contributed to writing the manuscript. Overall GIS modelling is done by Shubham Badola. Ravinder Singh and Surya Parkash provided expert guidance in selecting the causative factors for the study and offered overall guidance for writing the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Shubham Badola.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKeefer, D. K. (1984). Landslides caused by earthquakes. Geological Society of America Bulletin, 95(4), 406-421. \u003c/li\u003e\n\u003cli\u003eHansen, A. (1984). Landslide hazard analysis. Slope instability. Brunsden, D., Prior, E., Eds.; Wiley: New York, NY, USA, 1984; pp. 523\u0026ndash;602. \u003c/li\u003e\n\u003cli\u003eDai, F. C., Lee, C. F., \u0026amp; Ngai, Y. Y. (2002). Landslide risk assessment and management: an overview. Engineering geology, 64(1), 65-87. \u003c/li\u003e\n\u003cli\u003eDahal, R. 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Int J Res Anal Rev, 6(1), 911-926.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Frequency Ratio, Landslide susceptibility map, Risk Probability, ROC-AUC, Garhwal Himalaya","lastPublishedDoi":"10.21203/rs.3.rs-4575738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4575738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLandslides is deadliest disasters which occur frequently without warning causing damages and human causalities in the vulnerable areas. The topography of the region affects the frequency of landslides occurrences, as well as the impact of outside factors including intense rain, seismic activity, changes in groundwater levels, snowmelt, stream erosion, flooding, or any combination of these natural events. The research study investigates the risk probability of Garhwal Himalaya with the help of several causative factors, including slope, aspect, curvature, elevation, proximity to river, proximity to road, rainfall, lineament density, NDVI, NDBI and census data of 2011. Landslide inventory was prepared and classified into training data (70%) and testing data (30%). The landslide risk probability and susceptibility for the area of interest have been obtained using the frequency ratio (FR) approach. The resultant susceptibility and risk probability maps were classified into five i.e very low, low, medium, high, very high. The study reveals that 15.8% of the areas fall under the very high susceptibility zone, while 17.3% area in the very high risk zone. Further, the receiver operating characteristic-area under the curve (ROC-AUC) was used to calculate the landslide risk probability map's overall model accuracy, that turned up to 75.25%. The findings can be used further by planners and relevant authorities for landslip mitigation and control.\u003c/p\u003e","manuscriptTitle":"Assessing Landslide Risk Probability in the Garhwal Himalayas, India Using a GIS-Based Bivariate Statistical Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 08:59:37","doi":"10.21203/rs.3.rs-4575738/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68525424-1158-4382-a7ca-95632ff03220","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-18T16:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 08:59:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4575738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4575738","identity":"rs-4575738","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}