{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:19:30Z","timestamp":1780431570417,"version":"3.54.1"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,16]],"date-time":"2017-01-16T00:00:00Z","timestamp":1484524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["41671380"],"award-info":[{"award-number":["41671380"]}]},{"name":"Key project of National Nature Science Foundation of China","award":["41531180"],"award-info":[{"award-number":["41531180"]}]},{"name":"Foundation of Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geoinformation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.<\/jats:p>","DOI":"10.3390\/ijgi6010018","type":"journal-article","created":{"date-parts":[[2017,1,16]],"date-time":"2017-01-16T09:44:02Z","timestamp":1484559842000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping"],"prefix":"10.3390","volume":"6","author":[{"given":"Qianqian","family":"Ba","sequence":"first","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susu","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Environment and Resource, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianjiao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11069-012-0347-6","article-title":"Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling\u2013Narayanghat road section in Nepal Himalaya","volume":"65","author":"Devkota","year":"2013","journal-title":"Nat. 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