[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-39437-en":3,"doc-seo-39437-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},39437,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Landslide Susceptibility Mapping of High-Risk Peripheral Slopes Around Bageshwar City Using Frequency Ratio and Random Forest Techniques","Landslide susceptibility mapping (LSM) supports natural-hazard management for slope failures in hazard-prone Himalayan terrain. Prior work often emphasizes regional or road-cut scales, limiting coverage of vulnerable elements in high-risk settlements. This study maps susceptibility for slopes around Bageshwar city periphery using frequency ratio (FR) and random forest (RF) models. Fifteen conditioning factors are considered, including terrain, hydrologic, geological, land-use, rainfall, road and vegetation variables, with field-derived Schmidt hammer rebound (SHR). Model performance is evaluated with ROC curves and AUC, showing higher predictive ability for RF than FR and enabling safer infrastructure planning.","Geological Journal   \n RESEARCH ARTICLE  \nLandslide Susceptibility Mapping of High-Risk Peripheral Slopes Around Bageshwar City Using Frequency Ratio and Random Forest Techniques  \nAtifAhamad | Tariq Siddique  | Khyati Gupta  \nDepartment of Geology, Aligarh Muslim University, Aligarh, India  \nCorrespondence: Tariq Siddique ([tariqsiddiqueiitr@gmail.com](tariqsiddiqueiitr@gmail.com); [tsiddique@myamu.ac.in](tsiddique@myamu.ac.in))  \nReceived: 23 July 2025 | Revised: 16 January 2026 | Accepted: 16 March 2026  \nAcademic Editor: Sansar Raj Meena  \nKeywords: Bageshwar | frequency ratio | Himalaya | landslide susceptibility mapping | landslides | random forest  \nABSTRACT  \nLandslide susceptibility mapping (LSM) is a vital tool for managing natural hazards related to slope failures. In Himalaya, most of the LSM studies are confined to either regional scale or along road cut sections. The associated risk factor in such studies is less due to limited number of vulnerable elements at risk. This study focuses on slopes bordering the periphery of Bageshwar city, setting a paradigm for LSM in high-risk settlements in the Himalaya. Frequency ratio (FR) and Random forest (RF) models were applied and compared. Fifteen conditioning factors pertaining landslides were utilised in the present susceptibility analysis, which incorporates elevation, plan curvature, slope, stream power index (SPI), profile curvature, slope aspect, topographic wetness index (TWI), distance to stream, lithology, land use land cover (LULC), Schmidt hammer rebound (SHR) values, distance to fault, rainfall, distance to road and normalised difference vegetation index (NDVI) . The use of SHR values as one of the conditioning factors has been included as field-generated parameter, which is important in geotechnical engineering. The predictive accuracy of the applied models was assessed by generating receiver operating characteristic (ROC) curves and computing the area under the curve (AUC), as the measures of performance of applied models. The analysis demonstrates that AUC for the success rate curve (SRC) is 90.3% for FR method and 94.8% for RF method. AUC for the prediction rate curve (PRC) is 92% for FR method and 98% for RF method. This depicts that the RF model is relatively better than FR model. This research significantly contributes to proactive disaster risk reduction and safely infrastructure development in high-risk Himalayan cities by identifying critical susceptibility zones.  \n1 | Introduction  \nLandslides occur frequently in the orogenic belts of the world, emerging as a major concern in the era of ever-rising climate change. In past few decades, loss due to landslides in the form of highway damage, water damming and its release, infrastructure damage, loss of lives and ecological loss have intensified manifolds. With extensive variations in dynamics of climatic conditions, landslide disasters are increasing around the world and are escalating at an alarming rate. Landslides represent a prevalent  \nsevere natural hazard having enormous adverse impacts, particularly in the Himalayan terrain, where it causes extensive damage to lives, physical assets like transportation, public utilities and the ecosystem (Aleotti and Chowdhury 1999; Shrestha et al. 2025; Chauhan et al. 2025) . Landslide phenomena is controlled by a complex set of natural geo-environmental factors, which is augmented by anthropogenic influence (Ramesh and Anbazhagan 2015; Siddique et al. 2021; Haris and Siddique 2025) . Himalayan landslides are closely correlated with groundwater dynamics, with heavy monsoon rainfall being a primary trigger.  \n© 2026 John Wiley & Sons Ltd.  \nGeological Journal, 2026; 0:1–21 1  \n[https://doi.org/10.1002/gj.70281](https://doi.org/10.1002/gj.70281)  \nThe intense rain causes the groundwater table to rise, which increases pore pressure within the soil and rock, thereby reducing the effective stress and cause reduction in shear strength (Gupta, Biswas, et al. 2021; Se","cbCaiqpo8tqs4h6b","https://ap.wps.com/l/cbCaiqpo8tqs4h6b","pdf",10699307,1,21,"English","en",105,"# Introduction\n## Landslides and drivers in the Himalaya\n## Motivation and focus of this study","[{\"question\":\"What is the main goal of the study on Bageshwar city slopes?\",\"answer\":\"To generate landslide susceptibility mapping for high-risk peripheral slopes around Bageshwar city using frequency ratio and random forest models.\"},{\"question\":\"Which conditioning factors are used in the susceptibility analysis?\",\"answer\":\"Fifteen factors are used, including elevation, plan/profile curvature, slope and aspect, stream power index, TWI, distance to stream, lithology, LULC, SHR, distance to fault, rainfall, distance to road, and NDVI.\"},{\"question\":\"How are the FR and RF models evaluated for predictive performance?\",\"answer\":\"Performance is assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC) for success rate and prediction rate curves.\"}]",1783082780,53,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"landslide-susceptibility-mapping-of-high-risk-peripheral-slopes-around-bageshwar-city-using-frequency-ratio-and-random-forest-techniques","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/landslide-susceptibility-mapping-of-high-risk-peripheral-slopes-around-bageshwar-city-using-frequency-ratio-and-random-forest-techniques/39437/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-13","2026-07-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study on Bageshwar city slopes?","Question",{"text":75,"@type":76},"To generate landslide susceptibility mapping for high-risk peripheral slopes around Bageshwar city using frequency ratio and random forest models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which conditioning factors are used in the susceptibility analysis?",{"text":80,"@type":76},"Fifteen factors are used, including elevation, plan/profile curvature, slope and aspect, stream power index, TWI, distance to stream, lithology, LULC, SHR, distance to fault, rainfall, distance to road, and NDVI.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the FR and RF models evaluated for predictive performance?",{"text":84,"@type":76},"Performance is assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC) for success rate and prediction rate 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