{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:31:37Z","timestamp":1774485097935,"version":"3.50.1"},"reference-count":99,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T00:00:00Z","timestamp":1582588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41807192"],"award-info":[{"award-number":["41807192"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.<\/jats:p>","DOI":"10.3390\/sym12030325","type":"journal-article","created":{"date-parts":[[2020,2,26]],"date-time":"2020-02-26T04:18:29Z","timestamp":1582690709000},"page":"325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping"],"prefix":"10.3390","volume":"12","author":[{"given":"Guirong","family":"Wang","sequence":"first","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxiang","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-6947","authenticated-orcid":false,"given":"Himan","family":"Shahabi","sequence":"additional","affiliation":[{"name":"Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"},{"name":"Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9668-8687","authenticated-orcid":false,"given":"Ataollah","family":"Shirzadi","sequence":"additional","affiliation":[{"name":"Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,25]]},"reference":[{"key":"ref_1","first-page":"12","article-title":"Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg\u2013Marquardt and Bayesian regularized neural networks","volume":"171","author":"Pradhan","year":"2012","journal-title":"Geomorphology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s12517-009-0089-2","article-title":"Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models","volume":"3","author":"Pradhan","year":"2010","journal-title":"Arab. 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