{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:22:38Z","timestamp":1780064558296,"version":"3.54.0"},"reference-count":85,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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"}]},{"name":"Austrian Science Fund (FWF) through the Doctoral College GIScience","award":["DK W 1237-N23"],"award-info":[{"award-number":["DK W 1237-N23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity and infrastructure. Recognizing this threat has recently led the Iranian geomorphology community to focus on the problem across the whole country. This study is in line with other efforts where the optimal method to map gully-prone areas is sought by testing state-of-the-art machine learning tools. In this study, we compare the performance of three machine learning algorithms, namely Fisher\u2019s linear discriminant analysis (FLDA), logistic model tree (LMT) and na\u00efve Bayes tree (NBTree). We also introduce three novel ensemble models by combining the aforementioned base classifiers to the Random SubSpace (RS) meta-classifier namely RS-FLDA, RS-LMT and RS-NBTree. The area under the receiver operating characteristic (AUROC), true skill statistics (TSS) and kappa criteria are used for calibration (goodness-of-fit) and validation (prediction accuracy) datasets to compare the performance of the different algorithms. In addition to susceptibility mapping, we also study the association between gully erosion and a set of morphometric, hydrologic and thematic properties by adopting the evidential belief function (EBF). The results indicate that hydrology-related factors contribute the most to gully formation, which is also confirmed by the susceptibility patterns displayed by the RS-NBTree ensemble. The RS-NBTree is the model that outperforms the other five models, as indicated by the prediction accuracy (area under curve (AUC) = 0.898, Kappa = 0.748 and TSS = 0.697), and goodness-of-fit (AUC = 0.780, Kappa = 0.682 and TSS = 0.618). The analyses are performed with the same gully presence\/absence balanced modeling design. Therefore, the differences in performance are dependent on the algorithm architecture. Overall, the EBF model can detect strong and reasonable dependencies towards gully-prone conditions. The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data.<\/jats:p>","DOI":"10.3390\/rs12010140","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T04:43:03Z","timestamp":1578026583000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment"],"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, Jalal Ale Ahmad Highway, Tehran 9821, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-1422","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi\u2019an 710021, China"},{"name":"College of Geology and Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4348-7288","authenticated-orcid":false,"given":"Luigi","family":"Lombardo","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands"}],"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, Salzburg 5020, 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":[[2020,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.pce.2014.02.002","article-title":"Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District\u2014Zimbabwe","volume":"67","author":"Dube","year":"2014","journal-title":"Phys. Chem. Earth"},{"key":"ref_2","unstructured":"Tomlinson, R.F. (2007). Thinking About GIS: Geographic Information System Planning for Managers, ESRI, Inc.. [1st ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.geomorph.2013.08.021","article-title":"Gully erosion susceptibility assessment by means of GIS-based logistic regression: A case of Sicily (Italy)","volume":"204","author":"Conoscenti","year":"2014","journal-title":"Geomorphology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/0169-555X(92)90017-I","article-title":"Statistical models of fluvial systems","volume":"5","author":"Rhoads","year":"1992","journal-title":"Geomorphology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10346-006-0047-y","article-title":"Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models","volume":"4","author":"Lee","year":"2007","journal-title":"Landslides"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00254-003-0917-8","article-title":"A comparison of the GIS based landslide susceptibility assessment methods: Multivariate versus bivariate","volume":"45","author":"Doyuran","year":"2004","journal-title":"Environ. Geol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0013-7952(03)00143-1","article-title":"Data driven bivariate landslide susceptibility assessment using geographical information systems: A method and application to Asarsuyu catchment, Turkey","volume":"71","author":"Doyuran","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4035","DOI":"10.1002\/ldr.3151","article-title":"Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function\u2013logistic regression algorithm","volume":"29","author":"Arabameri","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.enggeo.2018.07.019","article-title":"Presenting logistic regression-based landslide susceptibility results","volume":"244","author":"Lombardo","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1007\/s11069-014-1285-2","article-title":"A test of transferability for landslides susceptibility models under extreme climatic events: Application to the Messina 2009 disaster","volume":"74","author":"Lombardo","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.envsoft.2017.08.003","article-title":"Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model","volume":"97","author":"Lombardo","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11069-005-5182-6","article-title":"Validation and evaluation of predictive models in hazard assessment and risk management","volume":"37","year":"2006","journal-title":"Nat. Hazards"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.geomorph.2011.03.001","article-title":"Integrating physical and empirical landslide susceptibility models using generalized additive models","volume":"129","author":"Goetz","year":"2011","journal-title":"Geomorphology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1007\/s00477-018-1518-0","article-title":"Point process-based modeling of multiple debris flow landslides using INLA: An application to the 2009 Messina disaster","volume":"2","author":"Lombardo","year":"2018","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1007\/s00704-016-1919-2","article-title":"Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: A comparison between GLM, GAM, MARS, and M-AHP methods","volume":"130","author":"Pourghasemi","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10346-012-0320-1","article-title":"Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study","volume":"10","author":"Cuartero","year":"2013","journal-title":"Landslides"},{"key":"ref_18","first-page":"1","article-title":"Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Na\u00efve Bayes Models","volume":"2012","author":"Pradhan","year":"2012","journal-title":"Math. Prob. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s11069-016-2443-5","article-title":"Presence-only approach to assess landslide triggering-thickness susceptibility: A test for the Mili catchment (north-eastern Sicily, Italy)","volume":"84","author":"Lombardo","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s12665-011-1477-y","article-title":"Ensemble-based landslide susceptibility maps in Jinbu area, Korea. In Terrigenous mass movements","volume":"1","author":"Lee","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.catena.2013.10.011","article-title":"A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping","volume":"114","author":"Althuwaynee","year":"2014","journal-title":"Catena"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s12665-015-4950-1","article-title":"Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran","volume":"75","author":"Pourghasemi","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s11069-015-1700-3","article-title":"An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran","volume":"79","author":"Zakerinejad","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2017.10.010","article-title":"Spatial modelling of gully erosion in Mazandaran Province, northern Iran","volume":"161","author":"Zabihi","year":"2018","journal-title":"Catena"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1002\/ldr.2227","article-title":"Quantitative mapping and assessment of environmentally sensitive areas to desertification in central Iran","volume":"7","author":"Jafari","year":"2016","journal-title":"Land Degrad. Dev."},{"key":"ref_27","unstructured":"IRIMO (2018, August 12). Summary Reports of Iran\u2019s Extreme Climatic Events. Ministry of Roads and Urban Development, Iran Meteorological Organization. Available online: www.cri.ac.ir."},{"key":"ref_28","unstructured":"GSI (2018, August 12). Geology Survey of Iran. Available online: http:\/\/www.gsi.ir\/Main\/Lang_en\/index.html."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.catena.2019.04.032","article-title":"Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques","volume":"180","author":"Arabameri","year":"2019","journal-title":"CATENA"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.jenvman.2018.11.110","article-title":"Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS","volume":"232","author":"Arabameri","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Pradhan, B., and Rezaei, K. (2019). Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosci. J., 1\u201318.","DOI":"10.1007\/s12303-018-0067-3"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Pradhan, B., Rezaei, K., and Lee, C.-W. (2019). Assessment of Landslide Susceptibility Using Statistical-and Artificial Intelligence-based FR\u2013RF Integrated Model and Multiresolution DEMs. Remote Sens., 11.","DOI":"10.3390\/rs11090999"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s11629-018-5168-y","article-title":"GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms","volume":"16","author":"Arabameri","year":"2019","journal-title":"J. Mt. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.scitotenv.2019.01.021","article-title":"A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran","volume":"660","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1214\/aoms\/1177698950","article-title":"Upper and lower probabilities induced by a multi valued mapping","volume":"38","author":"Dempster","year":"1967","journal-title":"Ann. Math. Stat."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shafer, G. (1976). A Mathematical Theory of Evidence, Princeton University Press.","DOI":"10.1515\/9780691214696"},{"key":"ref_37","first-page":"169","article-title":"Data Envelopment Analysis Approach to Two-group Classification Problems and an Experimental Comparison with Some Classification Models","volume":"36","author":"Bal","year":"2007","journal-title":"Hacet. J. Math. Stat."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2020). Gis-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Appl. Sci., 10.","DOI":"10.3390\/app10010016"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s10346-015-0593-2","article-title":"Linear discriminant analysis to describe the relationship between rainfall and landslides in Bogot\u00e1, Colombia","volume":"13","year":"2016","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Durrant, R.J., and Kaban, A. (2010, January 25\u201328). Compressed fisher linear discriminant analysis: Classification of randomly projected data. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/1835804.1835945"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10994-005-0466-3","article-title":"Logistic Model Trees","volume":"59","author":"Landwehr","year":"2005","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"CATENA"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Tuan","year":"2016","journal-title":"Landslides"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.jbiotec.2006.07.020","article-title":"LogitBoost classifier for discriminating thermophilic and mesophilic proteins","volume":"127","author":"Zhang","year":"2007","journal-title":"J. Biotechnol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.compeleceng.2019.02.015","article-title":"Network data management model based on Na\u00efve Bayes classifier and deep neural networks in heterogeneous wireless networks","volume":"75","author":"Wang","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.01.329","article-title":"Landslide spatial modelling using novel bivariate statistical based Na\u00efve Bayes, RBF Classifier, and RBF Network machine learning algorithms","volume":"663","author":"He","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1080\/19475705.2017.1289250","article-title":"GIS-based landslide susceptibility modelling: A comparative assessment of kernel logistic regression, Na\u00efve-Bayes tree, and alternating decision tree models","volume":"8","author":"Chen","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_48","first-page":"832","article-title":"The random subSpace method for constructing decision forests","volume":"20","author":"Barandiaran","year":"1998","journal-title":"IEEE"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rokach, L., and Maimon, O.Z. (2008). Data Mining with Decision Trees: Theory and Applications, World scientific. [2nd ed.].","DOI":"10.1142\/9789812771728"},{"key":"ref_50","unstructured":"Lewis, R.J. (2000, January 22\u201325). An introduction to classification and regression tree (CART) analysis. Proceedings of the Annual Meeting of the Society for Academic Emergency Medicine, San Francisco, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gey, S., and Nedelec, E. (2005). Model Selection for CART Regression Trees, IEEE.","DOI":"10.1109\/TIT.2004.840903"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Gashler, M., Giraud-Carrier, C., and Martinez, T. (2008, January 11\u201313). Decision tree ensemble: Small heterogeneous is better than large homogeneous. Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications, San Francisco, CA, USA.","DOI":"10.1109\/ICMLA.2008.154"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.scitotenv.2018.12.115","article-title":"GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches","volume":"658","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Pradhan, B., Rezaei, K., Saro, L., and Sohrabi, M. (2019). An ensemble model for landslide susceptibility mapping in a forested area. Geochem. Int., 1\u201318.","DOI":"10.1080\/10106049.2019.1585484"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2729","DOI":"10.5194\/nhess-16-2729-2016","article-title":"The propagation of inventory-based positional errors into statistical landslide susceptibility models","volume":"16","author":"Steger","year":"2016","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s10064-017-1004-9","article-title":"Prioritization of landslide conditioning factors and its spatial modeling in shangnan county, china using gis-based data mining algorithms","volume":"77","author":"Chen","year":"2018","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.jhydrol.2019.03.013","article-title":"Spatial prediction of groundwater potentiality using anfis ensembled with teaching-learning-based and biogeography-based optimization","volume":"572","author":"Chen","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.scitotenv.2019.05.312","article-title":"Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods","volume":"684","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"134979","DOI":"10.1016\/j.scitotenv.2019.134979","article-title":"Modeling flood susceptibility using data-driven approaches of na\u00efve bayes tree, alternating decision tree, and random forest methods","volume":"701","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chen, W., Hong, H., Panahi, M., Shahabi, H., Wang, Y., Shirzadi, A., Pirasteh, S., Alesheikh, A.A., Khosravi, K., and Panahi, S. (2019). Spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo). Appl. Sci., 9.","DOI":"10.3390\/app9183755"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Chen, W., Blaschke, T., Tiefenbacher, J.P., Pradhan, B., and Tien Bui, D. (2020). Gully Head-Cut Distribution Modeling Using Machine Learning Methods\u2014A Case Study of N.W. Iran. Water, 12.","DOI":"10.3390\/w12010016"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Pradhan, B., Pourghasemi, H., 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_63","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Cerda, A., Rodrigo-Comino, J., Pradhan, B., Sohrabi, M., Blaschke, T., and Tien Bui, D. (2019). Proposing a Novel Predictive Technique for Gully Erosion Susceptibility Mapping in Arid and Semi-arid Regions (Iran). Remote Sens., 11.","DOI":"10.3390\/rs11212577"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Cerda, A., and Tiefenbacher, J.P. (2019). Spatial Pattern Analysis and Prediction of Gully Erosion Using Novel Hybrid Model of Entropy-Weight of Evidence. Water, 11.","DOI":"10.3390\/w11061129"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Roy, J., Saha, S., Blaschke, T., Ghorbanzadeh, O., and Tien Bui, D. (2019). Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran. Remote Sens., 11.","DOI":"10.3390\/rs11243015"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Roy, J., Saha, S., Arabameri, A., Blaschke, T., and Bui, D.T. (2019). A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India. Remote Sens., 11.","DOI":"10.3390\/rs11232866"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Chen, W., Loche, M., Zhao, X., Li, Y., Lombardo, L., Cerda, A., Pradhan, B., and Bui, D.T. (2019). Comparison of machine learning models for gully erosion susceptibility mapping. Geosci. Front., in press.","DOI":"10.1016\/j.gsf.2019.11.009"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s11069-015-1915-3","article-title":"Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: Application to the 2009 storm event in Messina (Sicily, southern Italy)","volume":"79","author":"Lombardo","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.scitotenv.2019.02.017","article-title":"PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches","volume":"664","author":"Rahmati","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.enggeo.2009.12.004","article-title":"Techniques for evaluating the performance of landslide susceptibility models","volume":"111","author":"Frattini","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/j.1365-2664.2006.01214.x","article-title":"Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS)","volume":"43","author":"Allouche","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: The kappa statistic","volume":"22","author":"McHugh","year":"2012","journal-title":"Biochem. Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.scitotenv.2018.04.055","article-title":"GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models","volume":"634","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1016\/j.jhydrol.2019.05.089","article-title":"Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subSpace ensembles","volume":"575","author":"Chen","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s12665-016-6374-y","article-title":"Shallow landslide susceptibility assessment using a novel hybrid intelligence approach","volume":"76","author":"Shirzadi","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11069-016-2304-2","article-title":"Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS","volume":"83","author":"Pham","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Pham, T.B., Shirzadi, A., Shahabi, H., Omidvar, E., Singh, S.K., Sahana, M., Talebpour Asl, D., Bin Ahmad, B., Kim Quoc, N., and Lee, S. (2019). Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms. Sustainability, 11.","DOI":"10.3390\/su11164386"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s12524-016-0620-3","article-title":"Landslide hazard assessment using random subspace fuzzy rules based classifier ensemble and probability analysis of rainfall data: A case study at Mu Cang Chai district, Yen Bai province (Vietnam)","volume":"45","author":"Pham","year":"2017","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2014.05.013","article-title":"Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (lidar) data at catchment scale","volume":"152","author":"Jebur","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_81","unstructured":"Bui, D.T., Pradhan, B., Revhaug, I., and Tran, C.T. (2014). A comparative assessment between the application of fuzzy unordered rules induction algorithm and j48 decision tree models in spatial prediction of shallow landslides at lang son city, vietnam. Remote Sensing Applications in Environmental Research, Springer."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.3390\/s19112444","article-title":"A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)","volume":"19","author":"Bui","year":"2019","journal-title":"Sensors"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Chen, W., Shahabi, H., Zhang, S., Khosravi, K., Shirzadi, A., Chapi, K., Pham, B., 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_84","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at himalayan area (India) using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"Catena"},{"key":"ref_85","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/140\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:18:40Z","timestamp":1760361520000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":85,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12010140"],"URL":"https:\/\/doi.org\/10.3390\/rs12010140","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,1]]}}}