{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T04:35:41Z","timestamp":1782189341055,"version":"3.54.5"},"reference-count":90,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T00:00:00Z","timestamp":1582848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["DK W 1237-N23"],"award-info":[{"award-number":["DK W 1237-N23"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Na\u00efve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.<\/jats:p>","DOI":"10.3390\/s20051313","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T03:13:28Z","timestamp":1583205208000},"page":"1313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2739-3716","authenticated-orcid":false,"given":"Sunil","family":"Saha","sequence":"first","affiliation":[{"name":"Department of Geography, University of Gour Banga, Malda, West Bengal 732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagabandhu","family":"Roy","sequence":"additional","affiliation":[{"name":"Research Scholar, Dept. of Geography, University of Gour Banga, Malda, West Bengal 732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1142-1666","authenticated-orcid":false,"given":"Alireza","family":"Arabameri","sequence":"additional","affiliation":[{"name":"Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1007\/s12665-012-1634-y","article-title":"Assessing the susceptibility to water-induced soil erosion using a geomorphological, bivariate statistics-based approach","volume":"67","author":"Magliulo","year":"2012","journal-title":"Environ. 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