{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T02:20:29Z","timestamp":1780626029374,"version":"3.54.1"},"reference-count":83,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,8]],"date-time":"2020-06-08T00:00:00Z","timestamp":1591574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFC1502505"],"award-info":[{"award-number":["2017YFC1502505"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41807285"],"award-info":[{"award-number":["41807285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41762020"],"award-info":[{"award-number":["41762020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51879127"],"award-info":[{"award-number":["51879127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51769014"],"award-info":[{"award-number":["51769014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation of Jiangxi Province, China","award":["20192BAB216034"],"award-info":[{"award-number":["20192BAB216034"]}]},{"name":"National Science Foundation of Jiangxi Province, China","award":["20192ACB2102"],"award-info":[{"award-number":["20192ACB2102"]}]},{"name":"National Science Foundation of Jiangxi Province, China","award":["20192ACB20020"],"award-info":[{"award-number":["20192ACB20020"]}]},{"name":"Postdoctoral Science Foundation of China","award":["2019M652287"],"award-info":[{"award-number":["2019M652287"]}]},{"name":"Jiangxi Provincial Postdoctoral Science Foundation","award":["2019KY08"],"award-info":[{"award-number":["2019KY08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE as a geology and hydrology-related predisposing factor. The Ningdu County of China is taken as a research area. Firstly, 446 landslides are obtained through government disaster survey reports. Secondly, the SE amount in Ningdu County is calculated and nine other conventional predisposing factors are obtained under both 30 m and 60 m grid resolutions to determine the effects of SE on landslide susceptibility prediction. Thirdly, four types of machine-learning predictors with 30 m and 60 m grid resolutions\u2014C5.0 decision tree (C5.0 DT), logistic regression (LR), multilayer perceptron (MLP) and support vector machine (SVM)\u2014are applied to construct the landslide susceptibility prediction models considering the SE factor as SE-C5.0 DT, SE-LR, SE-MLP and SE-SVM models; C5.0 DT, LR, MLP and SVM models with no SE are also used for comparisons. Finally, the area under receiver operating feature curve is used to verify the prediction accuracy of these models, and the relative importance of all the 10 predisposing factors is ranked. The results indicate that: (1) SE factor plays the most important role in landslide susceptibility prediction among all 10 predisposing factors under both 30 m and 60 m resolutions; (2) the SE-based models have more accurate landslide susceptibility prediction than the single models with no SE factor; (3) all the models with 30 m resolutions have higher landslide susceptibility prediction accuracy than those with 60 m resolutions; and (4) the C5.0 DT and SVM models show higher landslide susceptibility prediction performance than the MLP and LR models.<\/jats:p>","DOI":"10.3390\/ijgi9060377","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T04:19:39Z","timestamp":1591676379000},"page":"377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models"],"prefix":"10.3390","volume":"9","author":[{"given":"Faming","family":"Huang","sequence":"first","affiliation":[{"name":"School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Du","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinsong","family":"Huang","sequence":"additional","affiliation":[{"name":"ARC Centre of Excellence for Geotechnical Science and Engineering, University of Newcastle, Newcastle, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghui","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhilu","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Li","sequence":"additional","affiliation":[{"name":"Changjiang Institute of Survey, Planning, Design and Research Co., Ltd., Wuhan 430010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,8]]},"reference":[{"key":"ref_1","first-page":"664","article-title":"Regional landslide susceptibility mapping based on grey relational degree model","volume":"44","author":"Huang","year":"2019","journal-title":"Earth Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.5194\/nhess-18-2161-2018","article-title":"Global fatal landslide occurrence from 2004 to 2016","volume":"18","author":"Froude","year":"2018","journal-title":"Nat. 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