[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-128832-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-128832-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","regional-scale-spatiotemporal-landslide-probability-assessment-through-machine-learning-and-potential-applications-for-operational-warning-systems-a-case-study-in-kvam-norway","Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems - a case study in Kvam (Norway)","","Machine learning models are widely used for landslide susceptibility mapping, yet their potential for spatiotemporal forecasting remains limited. This study proposes a dynamic space–time application of the random forest algorithm to estimate landslide hazard as a spatiotemporal probability of occurrence. Using a rainfall-induced landslide inventory with spatial and temporal detail from a region in Norway (Kvam), the method combines dynamic inputs such as cumulative rainfall and snowmelt with seasonal variability plus static geomorphic and lithologic parameters. Variable importance is analyzed to interpret model decisions and confirm consistency with triggering mechanisms. After training and testing on landslide and non-landslide samples across space and time, the model is applied to generate day-specific hazard maps before, during, and after selected events and validated with field observations. The approach extends beyond static ML applications and supports operational early-warning system perspectives.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/regional-scale-spatiotemporal-landslide-probability-assessment-through-machine-learning-and-potential-applications-for-operational-warning-systems-a-case-study-in-kvam-norway/128832/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/regional-scale-spatiotemporal-landslide-probability-assessment-through-machine-learning-and-potential-applications-for-operational-warning-systems-a-case-study-in-kvam-norway/128832.png","ImageObject",300,407,{"name":42,"@type":43},"Maeve","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-18","2026-08-06",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What gap does the study address in machine-learning landslide research?","Question",{"text":63,"@type":64},"Most machine-learning work focuses on spatial susceptibility, while spatiotemporal forecasting remains largely unexplored. The study targets dynamic space–time prediction of landslide probability.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is the random forest model set up in this research?",{"text":68,"@type":64},"The model is trained to estimate spatiotemporal landslide hazard using dynamic variables (e.g., cumulative rainfall, snowmelt, seasonal variability) together with static parameters such as lithology and morphologic attributes.",{"name":70,"@type":61,"acceptedAnswer":71},"How are model decisions validated and interpreted?",{"text":72,"@type":64},"Variable importance is assessed to interpret model logic and check alignment with physical landslide triggering mechanisms. Hazard maps generated for specific days are validated against field data.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},128832,1786003774,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":127,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":80,"read_time":143},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Landslides (2024) 21:2369–2387 DOI 10. 1007/s10346-024-02287-9 Received: 7 November 2023  \nAccepted: 20 May 2024  \nPublished online: 12 June 2024 The Author(s) 2024  \nNicola Nocentini · Ascanio Rosi · Luca Piciullo · Zhongqiang Liu · Samuele Segoni · Riccardo Fanti  \nRegional‑scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)  \nAbstract The use of machine learning models for landslide susceptibility mapping is widespread but limited to spatial prediction. The potential of employing these techniques inspatiotemporal landslide forecasting remains largely unexplored. To address this gap, this study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence) . An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. In this study, the variables’ importance was assessed and used to interpret the model decisions and to verify that they align with the physical mechanism responsible for landslide triggering. The algorithm, once trained and tested against landslide and non-landslide data sampled over space and time, produced a model predictor that was subsequently applied to the entire study area at different times: before, during, and after specific landslide events. For each selected day, a specific and space–time-dependent landslide hazard map was generated, then validated against field data. This study overcomes the traditional static applications of machine learning and demonstrates the applicability of a novel model aimed atspatiotemporal landslide probability assessment, with perspectives of applications to early warning systems.  \nKeywords Landslides · Spatiotemporal prediction · Landslide hazard maps · Warning · Machine learning · Random forest  \nIntroduction  \nMachine learning (ML) is a subset of artificial intelligence that focuses on developing algorithms that enable computers to learn from data, recognize patterns, and make decisions or predictions based on that learned information (Mitchell 1997; Bishop and Nasrabadi 2006; Hastie et al. 2001) . It has gained popularity in various applications, including geological hazard, in particular for assessing landslide susceptibility maps (LSMs) (Catani et al. 2013; Reichenbach et al. 2018; Crawford et al. 2021; Tehrani et al. 2022; Merghadi et al., 2020; Pham et al. 2016) .  \nThe physical mechanism of landslide triggering is very complex and influenced by several geological, hydrological, climatic, and  \nanthropogenic factors. Physically based models simulate the slope failure mechanism through rigorous mathematical equations but face difficulties in handling the spatial variability of geotechnical and hydrogeological soil properties over large areas, remaining applicable only at the slope scale (Vannocci et al. 2022; Alvioli and Baum 2016; Tran et al., 2018; Corominas et al. 2014) . In contrast, most Landslide Early Warning Systems (LEWSs) are based on rainfall thresholds (Guzzetti et al. 2020), which are defined as a rainfall value beyond which landslides are expected to occur (Guzzetti et al. 2008; Segoni et al. 2018a; Piciullo et al. 2018) . The strength of rainfall thresholds lies in their simplicity; in fact, they are typically based only on a single parameter, rainfall. Although physically based approaches are more accurate, rainfall thresholds are fast and sufficiently accurate for regional-scale predictions (Piciullo et al. 2018) and can easily be understood and i","cbCaiouyg1po9r4T","https://ap.wps.com/l/cbCaiouyg1po9r4T","pdf",7027925,"English","# Introduction\n## Machine learning in landslide susceptibility and hazard assessment\n## Physical models vs rainfall-threshold approaches\n## Dynamic (space–time) prediction and research gaps\n## Variable importance and interpretability in ML\n## From susceptibility to spatiotemporal probability","[{\"question\":\"What gap does the study address in machine-learning landslide research?\",\"answer\":\"Most machine-learning work focuses on spatial susceptibility, while spatiotemporal forecasting remains largely unexplored. The study targets dynamic space–time prediction of landslide probability.\"},{\"question\":\"How is the random forest model set up in this research?\",\"answer\":\"The model is trained to estimate spatiotemporal landslide hazard using dynamic variables (e.g., cumulative rainfall, snowmelt, seasonal variability) together with static parameters such as lithology and morphologic attributes.\"},{\"question\":\"How are model decisions validated and interpreted?\",\"answer\":\"Variable importance is assessed to interpret model logic and check alignment with physical landslide triggering mechanisms. Hazard maps generated for specific days are validated against field data.\"}]","Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems - a case study in Kvam (Norway) | PDF",48]