[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31941":3,"doc-seo-31941":27},{"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,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31941,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Landslide Susceptibility Mapping in Complex Topo-Climatic Himalayan Terrain, India Using Machine Learning Models: A Comparative Study of XGBoost, RF and ANN","Landslides threaten infrastructure and human life in the Himalayas, making accurate susceptibility mapping essential for planning and risk reduction. Floods through toe erosion and wildfires through this study’s conditioning factors motivate a multi-hazard perspective that integrates machine learning with geospatial analysis. Using 19 conditioning elements, including flood and forest-fire susceptibility factors, the study evaluates XGBoost, Random Forest (RF), and Artificial Neural Network (ANN) for landslide-prone area mapping. Regression checks multicollinearity, and ROC–AUC assesses model accuracy; XGBoost reaches 94%, RF 92%, and ANN 77%, supporting disaster-management prioritization.","cbCaij5vuIBwyaT9","https://ap.wps.com/l/cbCaij5vuIBwyaT9","pdf",3477565,1,20,"English","en","# Introduction\n## Multi-hazard disaster risk context\n## Study area and hazard relevance\n## Motivation and research focus","[{\"question\":\"Why is landslide susceptibility mapping important in the Himalayas?\",\"answer\":\"It supports informed decision-making and proactive planning by identifying areas prone to landslides, which threaten infrastructure and human life in a challenging terrain.\"},{\"question\":\"How does the study incorporate multi-hazard influences into landslide susceptibility?\",\"answer\":\"It integrates flood-related and forest-fire-related susceptibility as novel conditioning factors alongside other environmental and geomorphic variables when building the susceptibility map.\"},{\"question\":\"Which machine learning model performed best, and what accuracy was reported?\",\"answer\":\"XGBoost achieved the highest accuracy at 94%, followed by Random Forest (92%), while ANN reached 77% based on ROC–AUC evaluation.\"}]",1780520524,50,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"landslide-susceptibility-mapping-in-complex-topo-climatic-himalayan-terrain-india-using-machine-learning-models-a-comparative-study-of-xgboost-rf-and-ann","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/landslide-susceptibility-mapping-in-complex-topo-climatic-himalayan-terrain-india-using-machine-learning-models-a-comparative-study-of-xgboost-rf-and-ann/31941/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-06-03",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Why is landslide susceptibility mapping important in the Himalayas?","Question",{"text":73,"@type":74},"It supports informed decision-making and proactive planning by identifying areas prone to landslides, which threaten infrastructure and human life in a challenging terrain.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How does the study incorporate multi-hazard influences into landslide susceptibility?",{"text":78,"@type":74},"It integrates flood-related and forest-fire-related susceptibility as novel conditioning factors alongside other environmental and geomorphic variables when building the susceptibility map.",{"name":80,"@type":71,"acceptedAnswer":81},"Which machine learning model performed best, and what accuracy was reported?",{"text":82,"@type":74},"XGBoost achieved the highest accuracy at 94%, followed by Random Forest (92%), while ANN reached 77% based on ROC–AUC evaluation.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]