{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T12:43:52Z","timestamp":1769690632699,"version":"3.49.0"},"reference-count":87,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41977228"],"award-info":[{"award-number":["41977228"]}]},{"name":"National Natural Science Foundation of China","award":["2022SF-335"],"award-info":[{"award-number":["2022SF-335"]}]},{"name":"Key Research Program of Shaanxi","award":["41977228"],"award-info":[{"award-number":["41977228"]}]},{"name":"Key Research Program of Shaanxi","award":["2022SF-335"],"award-info":[{"award-number":["2022SF-335"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study aimed to explore and compare the application of current state-of-the-art machine learning techniques, including bagging (Bag) and rotation forest (RF), to assess landslide susceptibility with the base classifier best-first decision tree (BFT). The proposed two novel ensemble frameworks, BagBFT and RFBFT, and the base model BFT, were used to model landslide susceptibility in Zhashui County (China), which suffers from landslides. Firstly, we identified 169 landslides through field surveys and image interpretation. Then, a landslide inventory map was built. These 169 historical landslides were randomly classified into two groups: 70% for training data and 30% for validation data. Then, 15 landslide conditioning factors were considered for mapping landslide susceptibility. The three ensemble outputs were estimated with a receiver operating characteristic (ROC) curve and statistical tests, as well as a new approach, the improved frequency ratio accuracy. The areas under the ROC curve (AUCs) for the training data (success rate) of the three algorithms were 0.722 for BFT, 0.869 for BagBFT, and 0.895 for RFBFT. The AUCs for the validating groups (prediction rates) were 0.718, 0.834, and 0.872, respectively. The frequency ratio accuracy of the three models was 0.76163 for the BFT model, 0.92220 for the BagBFT model, and 0.92224 for the RFBFT model. Both BagBFT and RFBFT ensembles can improve the accuracy of the BFT base model, and RFBFT was relatively better. Therefore, the RFBFT model is the most effective approach for the accurate modeling of landslide susceptibility mapping (LSM). All three models can improve the identification of landslide-prone areas, enhance risk management ability, and afford more detailed information for land-use planning and policy setting.<\/jats:p>","DOI":"10.3390\/rs15041007","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T01:48:56Z","timestamp":1676252936000},"page":"1007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["GIS-Based Landslide Susceptibility Modeling: A Comparison between Best-First Decision Tree and Its Two Ensembles (BagBFT and RFBFT)"],"prefix":"10.3390","volume":"15","author":[{"given":"Jingyun","family":"Gui","sequence":"first","affiliation":[{"name":"Department of Geological Engineering, College of Geological Engineering and Geomatics, Chang\u2019An University, Xi\u2019an 710064, China"},{"name":"GESSMin Group, CINTECX, Department of Natural Resources and Environmental Engineering, University of Vigo, 36310 Vigo, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6540-2711","authenticated-orcid":false,"given":"Leandro Rafael","family":"Alejano","sequence":"additional","affiliation":[{"name":"GESSMin Group, CINTECX, Department of Natural Resources and Environmental Engineering, University of Vigo, 36310 Vigo, Spain"}]},{"given":"Miao","family":"Yao","sequence":"additional","affiliation":[{"name":"China Information Industry Engineering Investigation and Research Institute, Xi\u2019an 710001, China"}]},{"given":"Fasuo","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, College of Geological Engineering and Geomatics, Chang\u2019An University, Xi\u2019an 710064, China"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview","volume":"102","author":"Castellanos","year":"2008","journal-title":"Eng. 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