[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126200-en":3,"doc-seo-126200-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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":27,"seo_description":14,"update_tm":28,"read_time":29},126200,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Insights into landslide susceptibility - a comparative evaluation of multi-criteria analysis and machine learning techniques - comparative study","Landslides pose severe financial, environmental, and human losses worldwide. While landslide susceptibility mapping often uses multi-criteria analysis (MCA) and machine learning (ML), head-to-head comparisons remain limited, especially concerning predictor importance, performance metrics, and hyperparameter optimization. This study compares logistic regression, random forest, support vector machines, and MCA for Petrópolis, Brazil, using 29 geographic, geological, climatic, and anthropogenic predictors with feature importance and tuning, showing random forest delivers the strongest accuracy, ROC AUC, and F1 results.","Geomatics, Natural Hazards and Risk  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tgnh20)[www.tandfonline.com/journals/tgnh20](homepage: www.tandfonline.com/journals/tgnh20)  \nInsights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques  \nZuleide Ferreira, Bruna Almeida, Ana Cristina Costa, Manoel do Couto Fernandes & Pedro Cabral  \nTo cite this article: Zuleide Ferreira, Bruna Almeida, Ana Cristina Costa, Manoel do Couto Fernandes & Pedro Cabral (2025) Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques, Geomatics, Natural Hazards and Risk, 16: 1, 2471019, DOI: 10. 1080/19475705 .2025.2471019  \nTo link to this article: [https://doi.org/10.1080/19475705.2025.2471019](https://doi.org/10.1080/19475705.2025.2471019)  \n© 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  View supplementary material  |  |\n| --- | --- |\n|  Published online: 05 Mar 2025. |  |\n|  Submit your article to this journal  |  |\n|  | Article views: 186 |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tgnh20](https://www.tandfonline.com/action/journalInformation?journalCode=tgnh20)  \nGEOMATICS, NATURAL HAZARDS AND RISK 2025, VOL. 16, NO. 1, 2471019  \n[https://doi.org/10.1080/19475705.2025.2471019](https://doi.org/10.1080/19475705.2025.2471019)  \nInsights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques  \nZuleide Ferreiraa, b , Bruna Almeidac , Ana Cristina Costaa , Manoel do Couto Fernandesd  and Pedro Cabrale 􀀃   \naNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Lisbon, Portugal; bDepartment of Geomatics, Instituto Federal de Educa~ao, Ci^encia e Tecnologia do Tocantins, Palmas, Tocantins, Brazil; cMember of the Leibniz Association, Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany; dLaboratory of Cartography – Geography Department, Federal University of Rio de Janeiro – UFRJ, Rio de Janeiro, Brazil; eSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing, China  \nABSTRACT  \nLandslides threaten communities worldwide, resulting in financial, environmental, and human losses. Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods remain scarce, particularly regarding predictor importance, performance metrics, and hyperparameter optimization. This research addresses these gaps by comparing logistic regression (LR), random forest (RF), support vector machines (SVM), and MCA, focusing on landslide susceptibility in Petrpolis, Brazil. The ML models used 29 influencing factors, encompassing geographic, geological, climatic, and anthropogenic variables, where feature importance analysis and hyperparameter tuning were applied to identify the most significant predictors. RF achieved the highest performance, with an accuracy of 0.94, ROC AUC of 0.98, and F1 score of 0.94. SVM and LR also performed well, with ROC AUCs of 0.96 and 0.95 and F1 scores of 0.92 and 0.89, respectively. Conversely, MCA showed lower results, with an accuracy of 0.41, ROC AUC of 0 .41, and F1 score of 0.55. We attribute RF’s robustness to its adaptability to diverse variable types, reduced overfitting risk, and high predictive accuracy. These findings underscore RF’s strength in LSM and highlight ML’s potential to support urban planning and mitigate risks in landslide-prone areas.  \nARTICLE HISTORY  \nReceived 25 June 2024 Accepted 18 February 2025  \nKEYWORDS  \nGeospatial modelling; landslide-prone areas; hazard mapping; disaster risk reduc","cbCaip03MXeA910P","https://ap.wps.com/l/cbCaip03MXeA910P","pdf",3132997,1,33,"English","en",105,"# Introduction\n## Landslide susceptibility mapping and method comparison\n# Methods\n## Multi-criteria analysis and machine learning models\n## Predictors and feature importance\n## Hyperparameter optimization\n# Results\n## Performance metrics and model comparison\n## Predictor importance insights\n# Discussion\n## Interpretations and implications for risk reduction\n# Conclusion\n## Key findings and recommendations","[{\"question\":\"Which models are compared for landslide susceptibility mapping in this study?\",\"answer\":\"The study compares logistic regression, random forest, support vector machines, and multi-criteria analysis for mapping landslide susceptibility.\"},{\"question\":\"What dataset and predictors are used in the analysis?\",\"answer\":\"The models use 29 influencing factors covering geographic, geological, climatic, and anthropogenic variables, and apply feature importance analysis with hyperparameter tuning.\"},{\"question\":\"How did random forest perform compared with the other methods?\",\"answer\":\"Random forest achieved the highest performance (accuracy 0.94, ROC AUC 0.98, F1 0.94), outperforming SVM and logistic regression, while MCA showed much lower results (accuracy 0.41, ROC AUC 0.41, F1 0.55).\"}]","Insights into landslide susceptibility - a comparative evaluation of multi-criteria analysis and machine learning techniques - comparative study | PDF",1785903765,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"insights-into-landslide-susceptibility-a-comparative-evaluation-of-multi-criteria-analysis-and-machine-learning-techniques-comparative-study","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/insights-into-landslide-susceptibility-a-comparative-evaluation-of-multi-criteria-analysis-and-machine-learning-techniques-comparative-study/126200/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which models are compared for landslide susceptibility mapping in this study?","Question",{"text":76,"@type":77},"The study compares logistic regression, random forest, support vector machines, and multi-criteria analysis for mapping landslide susceptibility.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and predictors are used in the analysis?",{"text":81,"@type":77},"The models use 29 influencing factors covering geographic, geological, climatic, and anthropogenic variables, and apply feature importance analysis with hyperparameter tuning.",{"name":83,"@type":74,"acceptedAnswer":84},"How did random forest perform compared with the other methods?",{"text":85,"@type":77},"Random forest achieved the highest performance (accuracy 0.94, ROC AUC 0.98, F1 0.94), outperforming SVM and logistic regression, while MCA showed much lower results (accuracy 0.41, ROC AUC 0.41, F1 0.55).","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]