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Here, we applied four machine learning algorithms, adaptive boosting (AdaBoost), gradient-boosting decision tree (GBDT), multilayer perceptron (MLP), and random forest (RF), to predict the landslide susceptibility at 30 m spatial scale based on thirteen landslide conditioning factors (LCFs) in a landslide-vulnerable region. Based on inventory landslide points, the classification results were evaluated, and indicated that the performance of the RF (F1-score: 0.85, AUC: 0.92), AdaBoost (F1-score: 0.83, AUC: 0.91), and GBDT (F1-score: 0.83, AUC: 0.88) methods were significantly better than the MLP (F1-score: 0.76, AUC: 0.79) method. The results further indicated that the areas with high and very high landslide risk (susceptibility greater than 0.5) accounted for about 40% of the study region. All four models matched well and predicted similar spatial distribution patterns in landslide susceptibility, with the very high risk areas mostly distributed in the western and southeastern regions. Daoshi, Qingliangfeng, Jinnan, and Linglong towns have the highest landslide risk, with mean susceptibility levels greater than 0.5. The leading contributing factors to landslide susceptibility were slightly different for the four models; however, population density, distance to road, and relief amplitude were generally among the top leading factors for most towns. Our study provided significant information on the highly landslide-prone areas and the major contributing factors for decision-makers and policy planners, and suggested that different areas should take unique precautions to mitigate or avoid severe damage from landslide events.<\/jats:p>","DOI":"10.3390\/rs15071886","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T01:37:05Z","timestamp":1680485825000},"page":"1886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5764-2992","authenticated-orcid":false,"given":"Haiwei","family":"Yu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Pei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6544-5287","authenticated-orcid":false,"given":"Guangsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"College of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.enggeo.2005.11.007","article-title":"A multi-method approach to study the stability of natural slopes and landslide susceptibility mapping","volume":"82","author":"Fall","year":"2006","journal-title":"Eng. 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