{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:18:14Z","timestamp":1778635094563,"version":"3.51.4"},"reference-count":130,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"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>The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90\/10, 80\/20, 70\/30, 60\/40, and 50\/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50\/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50\/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.<\/jats:p>","DOI":"10.3390\/rs12172833","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T08:53:43Z","timestamp":1598950423000},"page":"2833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Novel Machine Learning Approaches for Modelling the Gully Erosion 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2077-9413","authenticated-orcid":false,"given":"Omid","family":"Asadi Nalivan","sequence":"additional","affiliation":[{"name":"Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Gorgan 3184761174, Iran"}],"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\/0000-0002-6323-4838","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\/0000-0002-9032-2198","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\/0000-0003-0409-8263","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\/0000-0001-9863-2054","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, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia"}],"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":[[2020,9,1]]},"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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