{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:25:15Z","timestamp":1768677915593,"version":"3.49.0"},"reference-count":89,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of Alborz province, Iran, using hybrid Machine Learning (ML) algorithms, i.e., k-fold cross validation and ML techniques of credal decision tree (CDT), Alternative Decision Tree (ADTree), and their ensemble method (CDT-ADTree), which have been state-of-the-art soft computing techniques rarely used in the case of landslide susceptibility assessments. In this study, 22 key landslide causative factors (LCFs) were considered to explore their spatial relationship to landslides, based on local geomorphological and geo-environmental influences. The Random Forest (RF) algorithm was used for the identification of variables importance of different LCFs that are more prone to landslide susceptibility. A receiver operation characteristics (ROC) curve with area under the curve (AUC), accuracy, precision, and robustness index was used to evaluate and compare landslide susceptibility models. The output of the model performance shows that the CDT-ADTree model is the more robust model for the landslide susceptibility where the AUC, accuracy, and precision are 0.981, 0.837, and 0.867, respectively, than the standalone model of CDT and ADTree model. Therefore, it is concluded that the CDT-ADTree ensemble model can be applied as a new promising technique for spatial prediction of the landslide in further studies.<\/jats:p>","DOI":"10.3390\/rs12203389","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T08:56:48Z","timestamp":1602838608000},"page":"3389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1142-1666","authenticated-orcid":false,"given":"Alireza","family":"Arabameri","sequence":"first","affiliation":[{"name":"Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebrahim","family":"Karimi-Sangchini","sequence":"additional","affiliation":[{"name":"Soil Conservation and Watershed Management Research Department, Lorestan Agricultural and Natural Resources Research and Education Center, AREEO, Khorramabad 6815144316, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-8007","authenticated-orcid":false,"given":"Subodh Chandra","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9032-2198","authenticated-orcid":false,"given":"Asish","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3617-6820","authenticated-orcid":false,"given":"Indrajit","family":"Chowdhuri","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0409-8263","authenticated-orcid":false,"given":"Saro","family":"Lee","sequence":"additional","affiliation":[{"name":"Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea"},{"name":"Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-6479","authenticated-orcid":false,"given":"Dieu","family":"Tien Bui","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Oh, H.-J., and Lee, S. 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