{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:48:42Z","timestamp":1775137722917,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T00:00:00Z","timestamp":1602288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003700","name":"Korea Institute of Geoscience and Mineral Resources","doi-asserted-by":"publisher","award":["Ministry of Science and ICT"],"award-info":[{"award-number":["Ministry of Science and ICT"]}],"id":[{"id":"10.13039\/501100003700","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km2 (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully.<\/jats:p>","DOI":"10.3390\/rs12203284","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility"],"prefix":"10.3390","volume":"12","author":[{"given":"Paramita","family":"Roy","sequence":"first","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-8007","authenticated-orcid":false,"given":"Subodh","family":"Chandra 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\/0000-0002-1142-1666","authenticated-orcid":false,"given":"Alireza","family":"Arabameri","sequence":"additional","affiliation":[{"name":"Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6323-4838","authenticated-orcid":false,"given":"Rabin","family":"Chakrabortty","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea"},{"name":"Center of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah 21589, Saudi Arabia"},{"name":"Earth Observation Center, Institute of Climate Change, University Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia"}],"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":"Department of Geophysical Exploration, 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":[[2020,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Poesen, J., Torri, D., and Vanwalleghem, T. 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