{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T02:00:05Z","timestamp":1782439205129,"version":"3.54.5"},"reference-count":160,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"VSB\u2014Technical University of Ostrava","award":["SP2022\/21"],"award-info":[{"award-number":["SP2022\/21"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value &gt; 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.<\/jats:p>","DOI":"10.3390\/rs14133029","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"3029","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":207,"title":["Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey"],"prefix":"10.3390","volume":"14","author":[{"given":"Moziihrii","family":"Ado","sequence":"first","affiliation":[{"name":"Department of Information Technology, North-Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2371-9942","authenticated-orcid":false,"given":"Khwairakpam","family":"Amitab","sequence":"additional","affiliation":[{"name":"Department of Information Technology, North-Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3320-9965","authenticated-orcid":false,"given":"Arnab Kumar","family":"Maji","sequence":"additional","affiliation":[{"name":"Department of Information Technology, North-Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2433-3873","authenticated-orcid":false,"given":"El\u017cbieta","family":"Jasi\u0144ska","sequence":"additional","affiliation":[{"name":"Department of Operations Research and Business Intelligence, Wroc\u0142aw University of Science and Technology, 50-370 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1125-3305","authenticated-orcid":false,"given":"Radomir","family":"Gono","sequence":"additional","affiliation":[{"name":"Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB\u2014Technical University of Ostrava, 708 00 Ostrava, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2388-3710","authenticated-orcid":false,"given":"Zbigniew","family":"Leonowicz","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0983-2562","authenticated-orcid":false,"given":"Micha\u0142","family":"Jasi\u0144ski","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","unstructured":"Turner, A.K., and Schuster, R.L. 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