{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T18:59:10Z","timestamp":1778353150083,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:00:00Z","timestamp":1604966400000},"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>Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harmful consequences. The principle goal of the present research study is to develop GES maps for the Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using a machine learning algorithm (MLA) of boosted regression tree (BRT), bagging and the ensemble of BRT-bagging with K-fold cross validation (CV) resampling techniques. The combination of the aforementioned MLAs with resampling approaches is state-of-the-art soft computing, not often used in GES evaluation. In further progress of our research work, here we used a total of 20 gully erosion conditioning factors (GECFs) and a total of 199 gully head cut points for modelling GES. The variables\u2019 importance, which is responsible for gully erosion, was determined based on the random forest (RF) algorithm among the several GECFs used in this study. The output result of the model\u2019s performance was validated through a receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) statistical analysis. The predicted result shows that the ensemble of BRT-bagging is the most well fitted for GES where AUC value in K-3 fold is 0.972, whereas the value of AUC in sensitivity, specificity, PPV and NPV is 0.94, 0.93, 0.96 and 0.93, respectively, in a training dataset, and followed by the bagging and BRT model. Thus, from the predictive performance of this research study it is concluded that the ensemble of BRT-Bagging can be applied as a new approach for further studies in spatial prediction of GES. The outcome of this work can be helpful to policy makers in implementing remedial measures to minimize damages caused by gully erosion.<\/jats:p>","DOI":"10.3390\/rs12223675","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T14:10:41Z","timestamp":1605017441000},"page":"3675","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-8007","authenticated-orcid":false,"given":"Subodh Chandra","family":"Pal","sequence":"first","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1142-1666","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\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2013Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"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\/0000-0002-9032-2198","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\/0000-0002-6323-4838","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\/0000-0003-0409-8263","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-roYuseong-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\/0000-0002-6605-498X","authenticated-orcid":false,"given":"Shahab. S.","family":"Band","sequence":"additional","affiliation":[{"name":"Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,10]]},"reference":[{"key":"ref_1","unstructured":"Pourghasemi, H.R., and Gokceoglu, C. (2019). 30\u2014Spatial modeling of gully erosion: A new ensemble of CART and GLM data-mining algorithms. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier."},{"key":"ref_2","unstructured":"Poesen, J., and Govers, G. (1990). Gully erosion in the loam belt of Belgium: Typology and control measures. Soil Erosion on Agricultural Land Proceedings of A Workshop Sponsored by the British Geomorphological Research Group, Coventry, UK, 1989, John Wiley & Sons Ltd."},{"key":"ref_3","unstructured":"Poesen, J.W. (1996). Contribution of gully erosion to sediment production on cultivated lands and rangelands. Proceedings of an International Symposium, Exeter, UK, 15\u201319 July 1996 No. 236, IAHS."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Demoulin, A. (2018). Gullies and closed depressions in the loess belt: Scars of human\u2013environment interactions. Landscapes and Landforms of Belgium and Luxembourg, Springer International Publishing. World Geomorphological Landscapes.","DOI":"10.1007\/978-3-319-58239-9"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S0341-8162(02)00143-1","article-title":"Gully erosion and environmental change: Importance and research needs","volume":"50","author":"Poesen","year":"2003","journal-title":"CATENA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s41324-017-0157-8","article-title":"Assessment of flood hazard in a riverine tract between Damodar and Dwarkeswar River, Hugli District, West Bengal, India","volume":"26","author":"Das","year":"2018","journal-title":"Spat. Inf. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s42452-019-0345-3","article-title":"Living with floods through geospatial approach: A case study of Arambag C.D. Block of Hugli District, West Bengal, India","volume":"1","author":"Das","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1016\/j.asr.2019.12.003","article-title":"Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India","volume":"65","author":"Chowdhuri","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Malik, S., and Pal, S.C. (2020). Application of 2D numerical simulation for rating curve development and inundation area mapping: A case study of monsoon dominated Dwarkeswar river. Int. J. River Basin Manag., 1\u201311.","DOI":"10.1080\/15715124.2020.1738447"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pal, S., and Shit, M. (2017). Application of RUSLE model for soil loss estimation of Jaipanda watershed, West Bengal. Spat. Inf. Res.","DOI":"10.1007\/s41324-017-0107-5"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s40808-018-0540-z","article-title":"Modeling of water induced surface soil erosion and the potential risk zone prediction in a sub-tropical watershed of Eastern India","volume":"5","author":"Pal","year":"2019","journal-title":"Model Earth Syst. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.asr.2019.04.033","article-title":"Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model","volume":"64","author":"Pal","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shit, P.K., Pourghasemi, H.R., and Bhunia, G.S. (2020). Understanding the morphology and development of a rill-gully: An Empirical study of Khoai Badland, West Bengal, India. Gully Erosion Studies from India and Surrounding Regions, Springer International Publishing. Advances in Science, Technology & Innovation.","DOI":"10.1007\/978-3-030-23243-6"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s12665-018-7434-2","article-title":"Susceptibility mapping of gully erosion using GIS-based statistical bivariate models: A case study from Ali Al-Gharbi District, Maysan Governorate, southern Iraq","volume":"77","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s12524-020-01110-x","article-title":"Assessing the Importance of static and dynamic causative factors on erosion potentiality using SWAT, EBF with uncertainty and plausibility, logistic regression and novel ensemble model in a sub-tropical environment","volume":"48","author":"Chakrabortty","year":"2020","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s11069-016-2239-7","article-title":"Gully erosion susceptibility mapping: The role of GIS-based bivariate statistical models and their comparison","volume":"82","author":"Rahmati","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2018.12.042","article-title":"Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms","volume":"340","author":"Amiri","year":"2019","journal-title":"Geoderma"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.scitotenv.2017.07.198","article-title":"Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling","volume":"609","author":"Pourghasemi","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.scitotenv.2019.02.436","article-title":"Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms","volume":"668","author":"Gayen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1016\/j.scitotenv.2016.10.176","article-title":"Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework","volume":"579","author":"Rahmati","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.scitotenv.2018.11.235","article-title":"Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models","volume":"655","author":"Azareh","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Asadi Nalivan, O., Chandra Pal, S., Chakrabortty, R., Saha, A., Lee, S., Pradhan, B., and Tien Bui, D. (2020). Novel machine learning approaches for modelling the gully erosion susceptibility. Remote Sens., 12.","DOI":"10.3390\/rs12172833"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Band, S.S., Janizadeh, S., Chandra Pal, S., Saha, A., Chakrabortty, R., Shokri, M., and Mosavi, A. (2020). Novel ensemble approach of Deep Learning Neural Network (DLNN) model and Particle Swarm Optimization (PSO) algorithm for prediction of gully erosion susceptibility. Sensors, 20.","DOI":"10.3390\/s20195609"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"363","DOI":"10.14358\/PERS.77.4.363","article-title":"A Genetic programming approach to estimate vegetation cover in the context of soil erosion assessment","volume":"77","author":"Puente","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Puente, C., Olague, G., Trabucchi, M., Arjona-Villica\u00f1a, P.D., and Soubervielle-Montalvo, C. (2019). Synthesis of vegetation indices using genetic programming for soil erosion estimation. Remote Sens., 11.","DOI":"10.3390\/rs11020156"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2018.05.007","article-title":"Burned area estimations derived from Landsat ETM+ and OLI data: Comparing genetic\/ programming with maximum likelihood and classification and regression trees","volume":"142","author":"Cabral","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1002\/ldr.3397","article-title":"GIS-based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi-arid environment: Golestan Province, NE Iran","volume":"30","author":"Kariminejad","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rout, J.K., Rout, M., and Das, H. (2020). Development of different machine learning ensemble classifier for gully erosion susceptibility in gandheswari watershed of West Bengal, India. Machine Learning for Intelligent Decision Science, Springer. Algorithms for Intelligent Systems.","DOI":"10.1007\/978-981-15-3689-2"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.scitotenv.2016.02.023","article-title":"Boosted regression tree model-based assessment of the impacts of meteorological drivers of hand, foot and mouth disease in Guangdong, China","volume":"553","author":"Zhang","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"104223","DOI":"10.1016\/j.catena.2019.104223","article-title":"Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling","volume":"183","author":"Arabameri","year":"2019","journal-title":"CATENA"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/j.scitotenv.2019.06.205","article-title":"Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility","volume":"688","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pourghasemi, H.R., and Rossi, M. (2019). Gully erosion modeling using GIS-based data mining techniques in Northern Iran: A comparison between boosted regression tree and multivariate adaptive regression spline. Natural Hazards GIS-Based Spatial Modeling Using Data Mining Techniques, Springer International Publishing. Advances in Natural and Technological Hazards Research.","DOI":"10.1007\/978-3-319-73383-8"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Janizadeh, S., Avand, M., Chen, W., Farzin, M., Omidvar, E., Shirzadi, A., Shahabi, H., Clague, J.J., and Jaafari, A. (2020). GIS-based gully erosion susceptibility mapping: A comparison of computational ensemble data mining models. Appl. Sci., 10.","DOI":"10.3390\/app10062039"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s40808-015-0001-x","article-title":"Modeling of potential gully erosion hazard using geo-spatial technology at Garbheta block, West Bengal in India","volume":"1","author":"Shit","year":"2015","journal-title":"Model Earth Syst. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"332","DOI":"10.4314\/ejesm.v5i4.2","article-title":"Mechanism of gully-head retreat\u2014A study at Ganganir Danga, Paschim Medinipur, West Bengal","volume":"5","author":"Shit","year":"2012","journal-title":"Ethiop. J. Environ. Stud. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/widm.1054","article-title":"Resampling methods","volume":"2","author":"Chernick","year":"2012","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Pradhan, B., Pourghasemi, H.R., Rezaei, K., and Kerle, N. (2018). Spatial modelling of gully erosion using GIS and R programing: A comparison among three data mining algorithms. Appl. Sci., 8.","DOI":"10.3390\/app8081369"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11069-010-9598-2","article-title":"Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)","volume":"56","author":"Conforti","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.grj.2016.09.001","article-title":"Gully erosion and freeze-thaw processes in clay-rich soils, Northeast Tennessee, USA","volume":"9\u201312","author":"Barnes","year":"2016","journal-title":"GeoResJ"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.catena.2016.06.031","article-title":"Effects of ephemeral gully erosion on soil degradation in a cultivated area in Sicily (Italy)","volume":"145","author":"Ollobarren","year":"2016","journal-title":"CATENA"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1016\/j.gsf.2019.11.009","article-title":"Comparison of machine learning models for gully erosion susceptibility mapping","volume":"11","author":"Arabameri","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, W., Shahabi, H., Zhang, S., Khosravi, K., Shirzadi, A., Chapi, K., Pham, B.T., Zhang, T., Zhang, L., and Chai, H. (2018). Landslide susceptibility modeling based on GIS and novel bagging-based kernel logistic regression. Appl. Sci., 8.","DOI":"10.3390\/app8122540"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hong, H., Xiaoling, G., and Hua, Y. (2016, January 26\u201328). Variable selection using mean decrease accuracy and mean decrease gini based on random forest. Proceedings of the 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS.2016.7883053"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_46","unstructured":"Elith, J., and Leathwick, J. (2020, November 01). Boosted Regression Trees for Ecological Modeling. Online Tutorial. Available online: http:\/\/cran.r-project.org\/web\/packages\/dismo\/vignettes\/brt.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)","volume":"163","author":"Hong","year":"2018","journal-title":"CATENA"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chakrabortty, R., Pal, S.C., Sahana, M., Mondal, A., Dou, J., Pham, B.T., and Yunus, A.P. (2020). Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India. Nat. Hazards.","DOI":"10.1007\/s11069-020-04213-3"},{"key":"ref_51","unstructured":"Hair, J.F. (2006). Multivariate Data Analysis, Pearson Prentice Hall."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1016\/j.earscirev.2018.08.006","article-title":"Subsurface erosion by soil piping: Significance and research needs","volume":"185","author":"Poesen","year":"2018","journal-title":"Earth Sci. Rev."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3675\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:31:23Z","timestamp":1760178683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3675"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,10]]},"references-count":52,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223675"],"URL":"https:\/\/doi.org\/10.3390\/rs12223675","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,10]]}}}