{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T15:57:28Z","timestamp":1770998248982,"version":"3.50.1"},"reference-count":108,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and na\u00efve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of \u201cwhere and why\u201d gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area.<\/jats:p>","DOI":"10.3390\/ijgi11070401","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:17:10Z","timestamp":1657844230000},"page":"401","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale"],"prefix":"10.3390","volume":"11","author":[{"given":"Sliman","family":"Hitouri","sequence":"first","affiliation":[{"name":"Geosciences Laboratory, Department of Geology, Faculty of Sciences, University Ibn Tofail, BP 133, Kenitra 14000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-5241","authenticated-orcid":false,"given":"Antonietta","family":"Varasano","sequence":"additional","affiliation":[{"name":"ITC-CNR, Construction Technologies Institute, National Research Council, 70124 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0019-6862","authenticated-orcid":false,"given":"Meriame","family":"Mohajane","sequence":"additional","affiliation":[{"name":"ITC-CNR, Construction Technologies Institute, National Research Council, 70124 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8089-0290","authenticated-orcid":false,"given":"Safae","family":"Ijlil","sequence":"additional","affiliation":[{"name":"Geo-Engineering and Environment Laboratory, Water Sciences and Environment Engineering Team, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes 50050, Morocco"}]},{"given":"Narjisse","family":"Essahlaoui","sequence":"additional","affiliation":[{"name":"Geo-Engineering and Environment Laboratory, Water Sciences and Environment Engineering Team, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes 50050, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7488-5591","authenticated-orcid":false,"given":"Sk Ajim","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Sciences, Aligarh Muslim University (AMU), Aligarh 202002, UP, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1112-1783","authenticated-orcid":false,"given":"Ali","family":"Essahlaoui","sequence":"additional","affiliation":[{"name":"Geo-Engineering and Environment Laboratory, Water Sciences and Environment Engineering Team, Department of Geology, Faculty of Sciences, Moulay Ismail University, Meknes 50050, Morocco"}]},{"given":"Quoc Bao","family":"Pham","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot City 820000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0006-2490","authenticated-orcid":false,"given":"Mirza","family":"Waleed","sequence":"additional","affiliation":[{"name":"Department of Geography, Hong Kong Baptist University, Hong Kong, China"}]},{"given":"Sasi Kiran","family":"Palateerdham","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, University of Rome \u201cLa Sapienza\u201d, 00138 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8043-6431","authenticated-orcid":false,"given":"Ana Cl\u00e1udia","family":"Teodoro","sequence":"additional","affiliation":[{"name":"Earth Sciences Institute (ICT) and Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Domazetovi\u0107, F., \u0160iljeg, A., Lon\u010dar, N., and Mari\u0107, I. (2019). Development of Automated Multicriteria GIS Analysis of Gully Erosion Susceptibility. Appl. Geogr., 112.","DOI":"10.1016\/j.apgeog.2019.102083"},{"key":"ref_2","first-page":"175","article-title":"Use of Remote Sensing to Map Gully Erosion along the Atbara River, Sudan","volume":"1","author":"Fadul","year":"1999","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","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. Earth Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2","DOI":"10.46850\/elni.2015.001","article-title":"Land Degradation Neutrality under the SDGs: National and International Implementation of the Land Degradation Neutral World Target","volume":"1","author":"Dooley","year":"2015","journal-title":"Elni. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20","DOI":"10.14214\/sf.1650","article-title":"Land Degradation Neutrality (LDN) in Drylands and beyond\u2014Where Has It Come from and Where Does It Go","volume":"51","author":"Safriel","year":"2017","journal-title":"Silva Fenn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1051\/agro:2007062","article-title":"Soil-Erosion and Runoff Prevention by Plant Covers. A Review","volume":"28","year":"2008","journal-title":"Agron. Sustain. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1080\/07352689891304249","article-title":"Soil Erosion Impact on Agronomic Productivity and Environment Quality","volume":"17","author":"Lal","year":"1998","journal-title":"Crit. Rev. Plant Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.catena.2013.09.004","article-title":"Soil Erosion in Gully Catchments Affected by Land-Levelling Measures in the Souss Basin, Morocco, Analysed by Rainfall Simulation and UAV Remote Sensing Data","volume":"113","author":"Peter","year":"2014","journal-title":"CATENA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jaridenv.2015.06.002","article-title":"Land Use and Climate Change Effects on Soil Erosion in a Semi-Arid Mountainous Watershed (High Atlas, Morocco)","volume":"122","author":"Simonneaux","year":"2015","journal-title":"J. Arid Environ."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s12517-021-07009-2","article-title":"Mapping Soil Erosion\u2013Prone Sites through GIS and Remote Sensing for the Tifnout Askaoun Watershed, Southern Morocco","volume":"14","author":"Tairi","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1007\/s12517-014-1464-1","article-title":"Soil Erosion Hazard Mapping Using Analytic Hierarchy Process and Logistic Regression: A Case Study of Haffouz Watershed, Central Tunisia","volume":"8","author":"Kachouri","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s12665-019-8658-5","article-title":"Identification of Soil Erosion-Susceptible Areas Using Fuzzy Logic and Analytical Hierarchy Process Modeling in an Agricultural Watershed of Burdwan District, India","volume":"78","author":"Saha","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Senouci, R., Taibi, N.-E., Teodoro, A.C., Duarte, L., Mansour, H., and Yahia Meddah, R. (2021). GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria. Sustainability, 13.","DOI":"10.3390\/su13020630"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Senanayake, S., Pradhan, B., Huete, A., and Brennan, J. (2020). Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka. Remote Sens., 12.","DOI":"10.3390\/rs12091483"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1080\/19475705.2017.1384406","article-title":"Soil Erosion Susceptibility Mapping for Current and 2100 Climate Conditions Using Evidential Belief Function and Frequency Ratio","volume":"8","author":"Tehrany","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_17","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. Manage."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s40710-019-00388-5","article-title":"Comparative Analysis between Morphometry and Geo-Environmental Factor Based Soil Erosion Risk Assessment Using Weight of Evidence Model: A Study on Jainti River Basin, Eastern India","volume":"6","author":"Hembram","year":"2019","journal-title":"Environ. Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"655","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_21","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.catena.2012.05.005","article-title":"Landslide Susceptibility Mapping Using Index of Entropy and Conditional Probability Models in GIS: Safarood Basin, Iran","volume":"97","author":"Pourghasemi","year":"2012","journal-title":"CATENA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s12145-018-0338-6","article-title":"Spatial Prediction of Soil Erosion Susceptibility: An Evaluation of the Maximum Entropy Model","volume":"11","author":"Pournader","year":"2018","journal-title":"Earth Sci. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2895","DOI":"10.1007\/s10661-012-2758-y","article-title":"Assessing Soil Quality Indicator under Different Land Use and Soil Erosion Using Multivariate Statistical Techniques","volume":"185","author":"Nosrati","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s41651-018-0015-9","article-title":"Soil Erosion Susceptibility Mapping with the Application of Logistic Regression and Artificial Neural Network","volume":"2","author":"Sarkar","year":"2018","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gholami, V., Sahour, H., and Hadian Amri, M.A. (2021). Soil Erosion Modeling Using Erosion Pins and Artificial Neural Networks. CATENA, 196.","DOI":"10.1016\/j.catena.2020.104902"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.catena.2017.12.027","article-title":"Spatial Soil Erosion Estimation Using an Artificial Neural Network (ANN) and Field Plot Data","volume":"163","author":"Gholami","year":"2018","journal-title":"CATENA"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dinh, T.V., Nguyen, H., Tran, X.-L., and Hoang, N.-D. (2021). Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification. Math. Probl. Eng., 2021.","DOI":"10.1155\/2021\/6647829"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1007\/s12665-021-09631-5","article-title":"Soil Erosion Susceptibility Assessment Using Logistic Regression, Decision Tree and Random Forest: Study on the Mayurakshi River Basin of Eastern India","volume":"80","author":"Ghosh","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/03736245.2020.1716838","article-title":"Soil Erosion Risk Assessment in the Umzintlava Catchment (T32E), Eastern Cape, South Africa, Using RUSLE and Random Forest Algorithm","volume":"103","author":"Phinzi","year":"2021","journal-title":"S. Afr. Geogr. J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1016\/j.scitotenv.2018.10.064","article-title":"An Ensemble Prediction of Flood Susceptibility Using Multivariate Discriminant Analysis, Classification and Regression Trees, and Support Vector Machines","volume":"651","author":"Choubin","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1080\/10106049.2019.1585482","article-title":"Landslide Susceptibility Mapping Using Na\u00efve Bayes and Bayesian Network Models in Umyeonsan, Korea","volume":"35","author":"Lee","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mosavi, A., Sajedi-Hosseini, F., Choubin, B., Taromideh, F., Rahi, G., and Dineva, A. (2020). Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models. Water, 12.","DOI":"10.3390\/w12071995"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lei, X., Chen, W., Avand, M., Janizadeh, S., Kariminejad, N., Shahabi, H., Costache, R., 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_35","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_36","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_37","doi-asserted-by":"crossref","unstructured":"Pal, S.C., Arabameri, A., Blaschke, T., Chowdhuri, I., Saha, A., Chakrabortty, R., Lee, S., and Band, S.S. (2020). Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility. Remote Sens., 12.","DOI":"10.3390\/rs12223675"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Soleimanpour, S.M., Pourghasemi, H.R., and Zare, M. (2021). A Comparative Assessment of Gully Erosion Spatial Predictive Modeling Using Statistical and Machine Learning Models. CATENA, 207.","DOI":"10.1016\/j.catena.2021.105679"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1080\/19475705.2020.1753824","article-title":"Gully Erosion Susceptibility Mapping Using Artificial Intelligence and Statistical Models","volume":"11","author":"Choi","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","unstructured":"Avand, J., Janizadeh, S., Naghibi, S.A., Pourghasemi, H.R., Bozchaloei, S.K., 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_42","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1080\/19475705.2020.1837968","article-title":"Head-Cut Gully Erosion Susceptibility Modelling Based on Ensemble Random Forest with Oblique Decision Trees in Fareghan Watershed, Iran","volume":"11","author":"Pham","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Chen, W., Lombardo, L., Blaschke, T., and Tien Bui, D. (2020). Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12010140"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ahmadpour, H., Bazrafshan, O., Rafiei-Sardooi, E., Zamani, H., and Panagopoulos, T. (2021). Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection. Sustainability, 13.","DOI":"10.3390\/su131810110"},{"key":"ref_45","first-page":"1","article-title":"Deep Learning and Boosting Framework for Piping Erosion Susceptibility Modeling: Spatial Evaluation of Agricultural Areas in the Semi-Arid Region","volume":"12","author":"Chen","year":"2021","journal-title":"Geocarto. Int."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, A., Wang, C., Pang, G., Long, Y., Wang, L., Cruse, R.M., and Yang, Q. (2021). Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100680"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s12665-021-09599-2","article-title":"Modeling Gully Erosion Susceptibility in Phuentsholing, Bhutan Using Deep Learning and Basic Machine Learning Algorithms","volume":"80","author":"Saha","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kadavi, P., Lee, C.-W., and Lee, S. (2018). Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens., 10.","DOI":"10.3390\/rs10081252"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s12040-006-0004-0","article-title":"Probabilistic Landslide Hazards and Risk Mapping on Penang Island, Malaysia","volume":"115","author":"Lee","year":"2006","journal-title":"J. Earth Syst. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Costache, R., Ngo, P.T.T., and Bui, D.T. (2020). Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping. Water, 12.","DOI":"10.3390\/w12061549"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ijlil, S., Essahlaoui, A., Mohajane, M., Essahlaoui, N., Mili, E.M., and Van Rompaey, A. (2022). Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System. Remote Sens., 14.","DOI":"10.3390\/rs14102379"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Cerda, A., Rodrigo-Comino, J., Pradhan, B., Sohrabi, M., Blaschke, T., and Bui, D.T. (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_53","doi-asserted-by":"crossref","unstructured":"Bouamrane, A., Bouamrane, A., and Abida, H. (2021). Water Erosion Hazard Distribution under a Semi-Arid Climate Condition: Case of Mellah Watershed, North-Eastern Algeria. Geoderma, 403.","DOI":"10.1016\/j.geoderma.2021.115381"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Nouayti, N., Cherif, E.K., Algarra, M., Pola, M.L., Fern\u00e1ndez, S., Nouayti, A., Esteves da Silva, J.C.G., Driss, K., Samlani, N., and Mohamed, H. (2022). Determination of Physicochemical Water Quality of the Ghis-Nekor Aquifer (Al Hoceima, Morocco) Using Hydrochemistry, Multiple Isotopic Tracers, and the Geographical Information System (GIS). Water, 14.","DOI":"10.3390\/w14040606"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1007\/s12517-022-10283-3","article-title":"Evaluation of Two Linear Kriging Methods for Piezometric Levels Interpolation and a Framework for Upgrading Groundwater Level Monitoring Network in Ghiss-Nekor Plain, North-Eastern Morocco","volume":"15","author":"Bouhout","year":"2022","journal-title":"Arab. J. Geosci."},{"key":"ref_56","unstructured":"Benabdelouahab, S., Salhi, A., Stitou, J., Himi, M., Draoui, M., and Casas, A. (2011, January 19\u201320). Application Des SIG et de La Tomographie \u00c9lectrique Pour Contribuer \u00e0 La Protection de l\u2019aquif\u00e8re de Martil-Alila (Maroc). Proceedings of the Euromediterranean Scientific Congress on Engineering, Algeciras, Spain."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bourjila, A., Dimane, F., EL Ouarghi, H., Nouayti, N., Taher, M., EL Hammoudani, Y., Saadi, O., and Bensiali, A. (2021). Groundwater Potential Zones Mapping by Applying GIS, Remote Sensing and Multi-Criteria Decision Analysis in the Ghiss Basin, Northern Morocco. Groundw. Sustain. Dev., 15.","DOI":"10.1016\/j.gsd.2021.100693"},{"key":"ref_58","first-page":"75","article-title":"The Global Change Impacts on Forest Natural Resources in Central Rif Mountains in Northern Morocco: Extensive Exploration and Planning Perspective","volume":"17","author":"Aissa","year":"2019","journal-title":"GOT\u2014J. Geogr. Spat. Plan."},{"key":"ref_59","first-page":"787","article-title":"About the Age of the Ketama Unit\u2019s Anchi-Epizonal Metamorphism, Central Rif, Morocco","volume":"313","author":"Leikine","year":"1991","journal-title":"Comptes Rendus\u2014Acad. Sci. Ser. II"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s12517-021-06973-z","article-title":"Spatial Assessment of the Vulnerability of Water Resources against Anthropogenic Pollution Using the DKPR Model: A Case of Ghiss-Nekkour Basin, Morocco","volume":"14","author":"Mansour","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"69","DOI":"10.12912\/27197050\/141525","article-title":"Impact of Anthropic Activities on the Quality of Groundwater in the Central Rif (North Morocco)","volume":"22","author":"Benyoussef","year":"2021","journal-title":"Ecol. Eng. Environ. Technol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"95","DOI":"10.7494\/geom.2022.16.2.95","article-title":"An Estimation of Soil Erosion Rate Hot Spots by Integrated USLE and GIS Methods: A Case Study of the Ghiss Dam and Basin in Northeastern Morocco","volume":"16","author":"Taher","year":"2022","journal-title":"Geomat. Environ. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1007\/s10346-016-0769-4","article-title":"Applying Information Theory and GIS-Based Quantitative Methods to Produce Landslide Susceptibility Maps in Nancheng County, China","volume":"14","author":"Tsangaratos","year":"2017","journal-title":"Landslides"},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.catena.2005.06.001","article-title":"Gully Erosion: Impacts, Factors and Control","volume":"63","author":"Valentin","year":"2005","journal-title":"CATENA"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s12518-020-00301-y","article-title":"Modelling of Gully Erosion Risk Using New Ensemble of Conditional Probability and Index of Entropy in Jainti River Basin of Chotanagpur Plateau Fringe Area, India","volume":"12","author":"Hembram","year":"2020","journal-title":"Appl. Geomat."},{"key":"ref_67","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_68","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_69","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital Terrain Modelling: A Review of Hydrological, Geomorphological, and Biological Applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","unstructured":"Chowdhuri, I., Pal, S.C., Saha, A., Chakrabortty, R., and Roy, P. (2021). Evaluation of Different DEMs for Gully Erosion Susceptibility Mapping Using In-Situ Field Measurement and Validation. Ecol. Inform., 65.","DOI":"10.1016\/j.ecoinf.2021.101425"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1007\/s11069-011-9844-2","article-title":"Landslide Susceptibility Analysis in the Hoa Binh Province of Vietnam Using Statistical Index and Logistic Regression","volume":"59","author":"Bui","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/10106049.2015.1041559","article-title":"Flood Susceptibility Mapping Using Frequency Ratio and Weights-of-Evidence Models in the Golastan Province, Iran","volume":"31","author":"Rahmati","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1007\/s00254-006-0256-7","article-title":"Landslide Susceptibility Mapping in the Damrei Romel Area, Cambodia Using Frequency Ratio and Logistic Regression Models","volume":"50","author":"Lee","year":"2006","journal-title":"Environ. Geol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s40808-018-0427-z","article-title":"Flood Susceptibility Mapping Using Geospatial Frequency Ratio Technique: A Case Study of Subarnarekha River Basin, India","volume":"4","author":"Samanta","year":"2018","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Mohajane, M., Costache, R., Karimi, F., Bao Pham, Q., Essahlaoui, A., Nguyen, H., Laneve, G., and Oudija, F. (2021). Application of Remote Sensing and Machine Learning Algorithms for Forest Fire Mapping in a Mediterranean Area. Ecol. Indic., 129.","DOI":"10.1016\/j.ecolind.2021.107869"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Boroughani, M., Pourhashemi, S., Hashemi, H., Salehi, M., Amirahmadi, A., Asadi, M.A.Z., and Berndtsson, R. (2020). Application of Remote Sensing Techniques and Machine Learning Algorithms in Dust Source Detection and Dust Source Susceptibility Mapping. Ecol. Inform., 56.","DOI":"10.1016\/j.ecoinf.2020.101059"},{"key":"ref_78","unstructured":"Breiman, L. (2001). Random Forests Machine Learning. Kluwer Academic Publishers."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The Random Subspace Method for Constructing Decision Forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1080\/13658816.2014.885527","article-title":"Predictive Modelling of Gold Potential with the Integration of Multisource Information Based on Random Forest: A Case Study on the Rodalquilar Area, Southern Spain","volume":"28","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/s12303-018-0067-3","article-title":"Spatial Prediction of Gully Erosion Using ALOS PALSAR Data and Ensemble Bivariate and Data Mining Models","volume":"23","author":"Arabameri","year":"2019","journal-title":"Geosci. J."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/978-3-642-20859-1_11","article-title":"An Enhanced Support Vector Machines Model for Classification and Rule Generation","volume":"Volume 356","author":"Koziel","year":"2011","journal-title":"Computational Optimization, Methods and Algorithms"},{"key":"ref_83","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_84","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1016\/j.scitotenv.2019.02.093","article-title":"Assessing the Performance of GIS- Based Machine Learning Models with Different Accuracy Measures for Determining Susceptibility to Gully Erosion","volume":"664","author":"Garosi","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Phinzi, K., Abriha, D., Bertalan, L., Holb, I., and Szab\u00f3, S. (2020). Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040252"},{"key":"ref_86","unstructured":"Ranjitha, K.V. (2018, January 28\u201330). Classification and Optimization Scheme for Text Data Using Machine Learning Na\u00efve Bayes Classifier. Proceedings of the 2018 IEEE World Symposium on Communication Engineering (WSCE), Singapore."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/j.dss.2012.11.015","article-title":"Partial Least Square Discriminant Analysis for Bankruptcy Prediction","volume":"54","year":"2013","journal-title":"Decis. Support Syst."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Pourghasemi, H., Gayen, A., Park, S., Lee, C.-W., and Lee, S. (2018). Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and Na\u00efveBayes Machine-Learning Algorithms. Sustainability, 10.","DOI":"10.3390\/su10103697"},{"key":"ref_89","first-page":"189","article-title":"Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils","volume":"6","author":"Bhargavi","year":"2009","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Jaafari, A., Avand, M., Al-Ansari, N., Dinh Du, T., Yen, H.P.H., Phong, T.V., Nguyen, D.H., Le, H.V., and Mafi-Gholami, D. (2020). Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry, 12.","DOI":"10.3390\/sym12061022"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1016\/j.mex.2019.10.031","article-title":"GIS Automated Multicriteria Analysis (GAMA) Method for Susceptibility Modelling","volume":"6","year":"2019","journal-title":"MethodsX"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"258","DOI":"10.7763\/IJMLC.2015.V5.517","article-title":"Filter Based Feature Selection Methods for Prediction of Risks in Hepatitis Disease","volume":"5","author":"Yildirim","year":"2015","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/TSMCB.2002.804363","article-title":"Orthogonal Forward Selection and Backward Elimination Algorithms for Feature Subset Selection","volume":"34","author":"Mao","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ipm.2004.08.006","article-title":"Information Gain and Divergence-Based Feature Selection for Machine Learning-Based Text Categorization","volume":"42","author":"Lee","year":"2006","journal-title":"Inf. Process. Manag."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3233\/IDA-1997-1302","article-title":"Feature Selection for Classification","volume":"1","author":"Dash","year":"1997","journal-title":"Intell. Data Anal."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"495","DOI":"10.32614\/RJ-2016-062","article-title":"Mctest: An R Package for Detection of Collinearity among Regressors","volume":"8","author":"Imdadullah","year":"2016","journal-title":"R J."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Salhi, A., Benabdelouahab, T., Martin-Vide, J., Okacha, A., El Hasnaoui, Y., El Mousaoui, M., El Morabit, A., Himi, M., Benabdelouahab, S., and Lebrini, Y. (2020). Bridging the Gap of Perception Is the Only Way to Align Soil Protection Actions. Sci. Total Environ., 718.","DOI":"10.1016\/j.scitotenv.2020.137421"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Phinzi, K., Holb, I., and Szab\u00f3, S. (2021). Mapping Permanent Gullies in an Agricultural Area Using Satellite Images: Efficacy of Machine Learning Algorithms. Agronomy, 11.","DOI":"10.3390\/agronomy11020333"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Lana, J.C., Castro, P.D., and Lana, C.E. (2022). Assessing Gully Erosion Susceptibility and Its Conditioning Factors in Southeastern Brazil Using Machine Learning Algorithms and Bivariate Statistical Methods: A Regional Approach. Geomorphology, 402.","DOI":"10.1016\/j.geomorph.2022.108159"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1080\/19475705.2021.1890644","article-title":"Robustness Analysis of Machine Learning Classifiers in Predicting Spatial Gully Erosion Susceptibility with Altered Training Samples","volume":"12","author":"Hembram","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Bouramtane, T., Hilal, H., Rezende-Filho, A.T., Bouramtane, K., Barbiero, L., Abraham, S., Valles, V., Kacimi, I., Sanhaji, H., and Torres-Rondon, L. (2022). Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil. Geosciences, 12.","DOI":"10.3390\/geosciences12060235"},{"key":"ref_102","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_103","doi-asserted-by":"crossref","unstructured":"Costache, R., Popa, M.C., Tien Bui, D., Diaconu, D.C., Ciubotaru, N., Minea, G., and Pham, Q.B. (2020). Spatial Predicting of Flood Potential Areas Using Novel Hybridizations of Fuzzy Decision-Making, Bivariate Statistics, and Machine Learning. J. Hydrol., 585.","DOI":"10.1016\/j.jhydrol.2020.124808"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.agrformet.2016.11.002","article-title":"A Hybrid Artificial Intelligence Approach Using GIS-Based Neural-Fuzzy Inference System and Particle Swarm Optimization for Forest Fire Susceptibility Modeling at a Tropical Area","volume":"233","author":"Bui","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.apenergy.2013.12.020","article-title":"A Naive Bayes Model for Robust Remaining Useful Life Prediction of Lithium-Ion Battery","volume":"118","author":"Ng","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Tuyen, T.T., Shirzadi, A., Pham, B.T., Shahabi, H., Omidvar, E., Amini, A., Entezami, H., Prakash, I., and Phong, T.V. (2019). Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction. Appl. Sci., 9.","DOI":"10.3390\/app9142824"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.jafrearsci.2018.06.028","article-title":"The Assessment of Soil Erosion Risk, Sediment Yield and Their Controlling Factors on a Large Scale: Example of Morocco","volume":"147","author":"Gourfi","year":"2018","journal-title":"J. Afr. Earth Sci."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s42452-019-0290-1","article-title":"Rainfall Distribution and Trends of the Daily Precipitation Concentration Index in Northern Morocco: A Need for an Adaptive Environmental Policy","volume":"1","author":"Salhi","year":"2019","journal-title":"SN Appl. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/7\/401\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:50:33Z","timestamp":1760140233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/7\/401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,14]]},"references-count":108,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["ijgi11070401"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11070401","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,14]]}}}