{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T04:33:59Z","timestamp":1786682039450,"version":"3.56.0"},"reference-count":67,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T00:00:00Z","timestamp":1633737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41977062"],"award-info":[{"award-number":["41977062"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)\u2019s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.<\/jats:p>","DOI":"10.3390\/ijgi10100680","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T21:23:25Z","timestamp":1633901005000},"page":"680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models"],"prefix":"10.3390","volume":"10","author":[{"given":"Annan","family":"Yang","sequence":"first","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"},{"name":"Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8576-2933","authenticated-orcid":false,"given":"Chunmei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"},{"name":"Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guowei","family":"Pang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"},{"name":"Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqing","family":"Long","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"},{"name":"Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"},{"name":"Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard M.","family":"Cruse","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Iowa State University, Ames, IA 50011, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinke","family":"Yang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi\u2019an 710127, China"},{"name":"Key Laboratory of National Forestry Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,9]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.earscirev.2016.07.009","article-title":"A century of gully erosion research: Urgency, complexity and study approaches","volume":"160","author":"Castillo","year":"2016","journal-title":"Earth-Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.catena.2012.03.001","article-title":"High resolution gully erosion and sedimentation processes, and land use changes since the Bronze Age and future trajectories in the Kazimierz Dolny area (Na\u0142\u0119cz\u00f3w Plateau, SE-Poland)","volume":"95","author":"Dotterweich","year":"2012","journal-title":"CATENA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geomorph.2012.10.031","article-title":"Impact of terrain attributes, parent material and soil types on gully erosion","volume":"186","author":"Chaplot","year":"2013","journal-title":"Geomorphology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1002\/esp.1866","article-title":"Gully processes and gully dynamics","volume":"34","author":"Kirkby","year":"2009","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.geomorph.2017.09.006","article-title":"Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion","volume":"298","author":"Rahmati","year":"2017","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.earscirev.2013.12.006","article-title":"A review of topographic threshold conditions for gully head development in different environments","volume":"130","author":"Torri","year":"2014","journal-title":"Earth-Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.catena.2005.06.003","article-title":"Spatial and temporal assessment of linear erosion in catchments under sloping lands of northern Laos","volume":"63","author":"Chaplot","year":"2005","journal-title":"CATENA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107547","DOI":"10.1016\/j.geomorph.2020.107547","article-title":"What is the best technique to estimate topographic thresholds of gully erosion? Insights from a case study on the permanent gullies of Rarh plain, India","volume":"375","author":"Majhi","year":"2021","journal-title":"Geomorphology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2014.08.010","article-title":"Predicting the susceptibility to gully initiation in data-poor regions","volume":"228","author":"Dewitte","year":"2015","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103637","DOI":"10.1016\/j.earscirev.2021.103637","article-title":"Measuring, modelling and managing gully erosion at large scales: A state of the art","volume":"218","author":"Vanmaercke","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1007\/s12665-018-7808-5","article-title":"GIS-based gully erosion susceptibility mapping: A comparison among three data-driven models and AHP knowledge-based technique","volume":"77","author":"Arabameri","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s11069-007-9188-0","article-title":"Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: A test in Southern Sicily","volume":"46","author":"Conoscenti","year":"2008","journal-title":"Nat. Hazards"},{"key":"ref_15","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-logistic regression algorithm","volume":"29","author":"Arabameri","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","unstructured":"Chowdhuri, I., Pal, S.C., Arabameri, A., Saha, A., Chakrabortty, R., Blaschke, T., Pradhan, B., and Band, S.S. (2020). Implementation of artificial intelligence based ensemble models for gully erosion susceptibility assessment. Remote. Sens., 12.","DOI":"10.3390\/rs12213620"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1080\/19475705.2021.1880977","article-title":"Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms","volume":"12","author":"Arabameri","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","unstructured":"Saha, S., Roy, J., Arabameri, A., Blaschke, T., and Tien Bui, D. (2020). Machine learning-based gully erosion susceptibility mapping: A case study of Eastern India. Sensors, 20.","DOI":"10.3390\/s20051313"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105158","DOI":"10.1016\/j.catena.2021.105158","article-title":"Grid order prediction of ephemeral gully head cut position: Regional scale application","volume":"200","author":"Wang","year":"2021","journal-title":"CATENA"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s10064-014-0607-7","article-title":"A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece)","volume":"74","author":"Polykretis","year":"2015","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jhydrol.2013.09.034","article-title":"Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS","volume":"504","author":"Tehrany","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107136","DOI":"10.1016\/j.geomorph.2020.107136","article-title":"A methodological comparison of head-cut based gully erosion susceptibility models: Combined use of statistical and artificial intelligence","volume":"359","author":"Arabameri","year":"2020","journal-title":"Geomorphology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-018-7844-1","article-title":"A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed, Morocco","volume":"77","author":"Meliho","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1002\/ldr.3112","article-title":"Assessment of land cover and land use change impact on soil loss in a tropical catchment by using multitemporal SPOT-5 satellite images and Revised Universal Soil Loss Equation model","volume":"29","author":"Nampak","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Shit, P.K., Pourghasemi, H.R., and Bhunia, G.S. (2020). Gully Erosion Susceptibility Mapping Based on Bayesian Weight of Evidence. Gully Erosion Studies from India and Surrounding Regions, Springer International Publishing.","DOI":"10.1007\/978-3-030-23243-6"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.geomorph.2012.02.019","article-title":"Gully and tunnel erosion in the hilly Loess Plateau region, China","volume":"153","author":"Zhu","year":"2012","journal-title":"Geomorphology"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Petovello, M.G., and Curran, J.T. (2017). Simulators and Test Equipment, Springer International Publishing.","DOI":"10.1007\/978-3-319-42928-1_18"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1016\/j.gsf.2020.03.005","article-title":"Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process","volume":"11","author":"Pourghasemi","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.geoderma.2018.05.027","article-title":"Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping","volume":"330","author":"Garosi","year":"2018","journal-title":"Geoderma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1002\/wics.84","article-title":"Multicollinearity","volume":"2","author":"Alin","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1080\/19475705.2014.984247","article-title":"A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping","volume":"7","author":"Pourghasemi","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1002\/env.999","article-title":"Incorporating uncertainty in gully erosion calculations using the random forest modelling approach","volume":"21","author":"Kuhnert","year":"2009","journal-title":"Environmetrics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s12665-017-6640-7","article-title":"A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China","volume":"76","author":"Xie","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_41","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_42","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R. News"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, Q., Jiang, Z., Wang, M., and Liu, K. (2021). Landslide and wildfire susceptibility assessment in southeast asia using ensemble machine learning methods. Remote. Sens., 13.","DOI":"10.3390\/rs13081572"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Song, Y., Niu, R., Shiluo, X., Ye, R., Peng, L., Guo, T., Li, S., and Chen, T. (2018). Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the Three Gorges Reservoir Area (China). ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010004"},{"key":"ref_45","unstructured":"Chen, T., and Guestrin, C. (, January 13\u201317August). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7323508","DOI":"10.1155\/2017\/7323508","article-title":"Comparative Analysis and Classification of Cassette Exons and Constitutive Exons","volume":"2017","author":"Cui","year":"2017","journal-title":"BioMed Res. Int."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1007\/s42452-020-3060-1","article-title":"Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest","volume":"2","author":"Sahin","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.compchemeng.2019.06.001","article-title":"Formation lithology classification using scalable gradient boosted decision trees","volume":"128","author":"Dev","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Can, R., Kocaman, S., and Gokceoglu, C. (2021). A Comprehensive assessment of XGBoost algorithm for landslide susceptibility mapping in the upper basin of Ataturk Dam, Turkey. Appl. Sci., 11.","DOI":"10.3390\/app11114993"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.enggeo.2005.02.002","article-title":"Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)","volume":"79","author":"Yesilnacar","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ding, H., Liu, K., Chen, X., Xiong, L., Tang, G., Qiu, F., and Strobl, J. (2020). Optimized segmentation based on the weighted aggregation method for loess bank gully mapping. Remote Sens., 12.","DOI":"10.3390\/rs12050793"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"145514","DOI":"10.1016\/j.scitotenv.2021.145514","article-title":"Effects of vegetation and climate on the changes of soil erosion in the Loess Plateau of China","volume":"773","author":"Jin","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.catena.2005.06.002","article-title":"Monitoring of gully erosion on the Loess Plateau of China using a global positioning system","volume":"63","author":"Wu","year":"2005","journal-title":"CATENA"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.catena.2019.02.010","article-title":"Effects of DEM resolution on the accuracy of gully maps in loess hilly areas","volume":"177","author":"Dai","year":"2019","journal-title":"CATENA"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1111\/tgis.12273","article-title":"Gully boundary extraction based on multidirectional hill-shading from high-resolution DEMs","volume":"21","author":"Yang","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lei, X., Chen, W., Avand, M., Janizadeh, S., Kariminejad, N., Shahabi, H., Costache, R.-D., Shahabi, H., Shirzadi, A., and Mosavi, A. (2020). GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote. Sens., 12.","DOI":"10.3390\/rs12152478"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Azedou, A., Lahssini, S., Khattabi, A., Meliho, M., and Rifai, N. (2021). A Methodological comparison of three models for gully erosion susceptibility mapping in the rural municipality of El Faid (Morocco). Sustainability, 13.","DOI":"10.3390\/su13020682"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1007\/s11442-021-1853-9","article-title":"Geomorphology-oriented digital terrain analysis: Progress and perspectives","volume":"31","author":"Xiong","year":"2021","journal-title":"J. Geogr. Sci."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"Avand, M., Janizadeh, S., Naghibi, S., Pourghasemi, H., Bozchaloei, S., and Blaschke, T. (2019). A Comparative assessment of random forest and k- nearest neighbor classifiers for gully erosion susceptibility mapping. Water, 11.","DOI":"10.3390\/w11102076"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bui, D., Shirzadi, A., Shahabi, H., Chapi, K., Omidvar, E., Pham, B., Talebpoor, D., Khaledian, h., Pradhan, B., and Panahi, M. (2019). A novel ensemble artificial intelligence approach for gully erosion mapping in a semi-arid watershed (Iran). Sensors, 19.","DOI":"10.3390\/s19112444"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Abedi, R., Costache, R., Shafizadeh-Moghadam, H., and Pham, Q.B. (2021). Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int., 1\u201318.","DOI":"10.1080\/10106049.2021.1920636"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"126370","DOI":"10.1016\/j.jhydrol.2021.126370","article-title":"Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models","volume":"598","author":"Barzegar","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"107563","DOI":"10.1016\/j.asoc.2021.107563","article-title":"An ensemble deep learning method as data fusion system for remote sensing multisensor classification","volume":"110","author":"Bigdeli","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"107045","DOI":"10.1016\/j.geomorph.2020.107045","article-title":"Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery","volume":"354","author":"Li","year":"2020","journal-title":"Geomorphology"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Band, S., Janizadeh, S., 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"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/10\/680\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:10:53Z","timestamp":1760166653000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/10\/680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,9]]},"references-count":67,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["ijgi10100680"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10100680","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,9]]}}}