{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:48:42Z","timestamp":1775137722899,"version":"3.50.1"},"reference-count":140,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,8,2]],"date-time":"2020-08-02T00:00:00Z","timestamp":1596326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Innovation Capability Support Program of Shaanxi","award":["\u200e2020KJXX-005\u200e"],"award-info":[{"award-number":["\u200e2020KJXX-005\u200e"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk Watershed in Iran. The assessment of gully erosion susceptibility was performed using four state-of-the-art data mining techniques: random forest (RF), credal decision trees (CDTree), kernel logistic regression (KLR), and best-first decision tree (BFTree). To the best of our knowledge, the KLR and CDTree algorithms have been rarely applied to gully erosion modeling. In the first step, from the 242 gully erosion locations that were identified, 70% (170 gullies) were selected as the training dataset, and the other 30% (72 gullies) were considered for the result validation process. In the next step, twelve gully erosion conditioning factors, including topographic, geomorphological, environmental, and hydrologic factors, were selected to estimate gully erosion susceptibility. The area under the ROC curve (AUC) was used to estimate the performance of the models. The results revealed that the RF model had the best performance (AUC = 0.893), followed by the KLR (AUC = 0.825), the CDTree (AUC = 0.808), and the BFTree (AUC = 0.789) models. Overall, the RF model performed significantly better than the others, which may support the application of this method to a transferable susceptibility model in other areas. Therefore, we suggest using the RF, KLR, and CDT models for gully erosion susceptibility mapping in other prone areas to assess their reproducibility.<\/jats:p>","DOI":"10.3390\/rs12152478","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T06:16:47Z","timestamp":1596435407000},"page":"2478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7927-831X","authenticated-orcid":false,"given":"Xinxiang","family":"Lei","sequence":"first","affiliation":[{"name":"College of Geology &amp; Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geology &amp; Environment, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-5051","authenticated-orcid":false,"given":"Mohammadtaghi","family":"Avand","sequence":"additional","affiliation":[{"name":"Department of Watershed Management Engineering and Sciences, Faculty of Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeid","family":"Janizadeh","sequence":"additional","affiliation":[{"name":"Department of Watershed Management Engineering and Sciences, Faculty of Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narges","family":"Kariminejad","sequence":"additional","affiliation":[{"name":"Department of Watershed &amp; Arid Zone Management, Gorgan University of Agricultural Sciences &amp; Natural Resources, Gorgan 49189-434, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3275-8436","authenticated-orcid":false,"given":"Hejar","family":"Shahabi","sequence":"additional","affiliation":[{"name":"Department of remote sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6876-8572","authenticated-orcid":false,"given":"Romulus","family":"Costache","sequence":"additional","affiliation":[{"name":"Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, Bucharest 013686, Romania"},{"name":"National Institute of Hydrology and Water Management, Bucure\u0219ti-Ploie\u0219ti Road, 97E, 1st District, Bucharest 013686, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5091-6947","authenticated-orcid":false,"given":"Himan","family":"Shahabi","sequence":"additional","affiliation":[{"name":"Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"},{"name":"Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9668-8687","authenticated-orcid":false,"given":"Ataollah","family":"Shirzadi","sequence":"additional","affiliation":[{"name":"Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"Amir","family":"Mosavi","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,2]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Blaschke, T., Pradhan, B., Pourghasemi, H.R., Tiefenbacher, J.P., and Bui, D.T. (2020). Evaluation of recent advanced soft computing techniques for gully erosion susceptibility mapping: A comparative study. Sensors, 20.","DOI":"10.3390\/s20020335"},{"key":"ref_3","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_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.jenvman.2019.06.102","article-title":"Flood susceptibility mapping in dingnan county (china) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm","volume":"247","author":"Wang","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J.J., Geertsema, M., Khosravi, K., Amini, A., and Bahrami, S. (2020). Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens., 12.","DOI":"10.3390\/rs12020266"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.agrformet.2018.12.015","article-title":"Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability","volume":"266","author":"Jaafari","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1002\/ldr.3255","article-title":"Sinkhole susceptibility mapping: A comparison between bayes-based machine learning algorithms","volume":"30","author":"Taheri","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.atmosres.2017.04.017","article-title":"Drought sensitivity mapping using two one-class support vector machine algorithms","volume":"193","author":"Roodposhti","year":"2017","journal-title":"Atmos. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Choubin, B., Soleimani, F., Pirnia, A., Sajedi-Hosseini, F., Alilou, H., Rahmati, O., Melesse, A.M., Singh, V.P., and Shahabi, H. (2019). Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns. Extreme Hydrology and Climate Variability, Elsevier.","DOI":"10.1016\/B978-0-12-815998-9.00017-8"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, S., Panahi, M., Pourghasemi, H.R., Shahabi, H., Alizadeh, M., Shirzadi, A., Khosravi, K., Melesse, A.M., Yekrangnia, M., and Rezaie, F. (2019). Sevucas: A novel gis-based machine learning software for seismic vulnerability assessment. Appl. Sci., 9.","DOI":"10.3390\/app9173495"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Alizadeh, M., Alizadeh, E., Kotenaee, S.A., Shahabi, H., Pour, A.B., Panahi, M., Ahmad, B.B., and Saro, L. (2018). Social vulnerability assessment using artificial neural network (ann) model for earthquake hazard in tabriz city, iran. Sustainability, 10.","DOI":"10.3390\/su10103376"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.3390\/s18082464","article-title":"Land subsidence susceptibility mapping in south korea using machine learning algorithms","volume":"18","author":"Bui","year":"2018","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1016\/j.gsf.2019.03.009","article-title":"Swpt: An automated gis-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors","volume":"10","author":"Rahmati","year":"2019","journal-title":"Geosci. Front."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.jhydrol.2018.08.027","article-title":"Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches","volume":"565","author":"Rahmati","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, Y., Tsangaratos, P., Shahabi, H., Ilia, I., Xue, W., and Bian, H. (2020). Groundwater spring potential mapping using artificial intelligence approach based on kernel logistic regression, random forest, and alternating decision tree models. Appl. Sci., 10.","DOI":"10.3390\/app10020425"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Rahmati, O., Falah, F., Shojaei, S., Al-Ansari, N., Shahabi, H., Shirzadi, A., G\u00f3rski, K., Nguyen, H., and Ahmad, B.B. (2020). Mapping of groundwater spring potential in karst aquifer system using novel ensemble bivariate and multivariate models. Water, 12.","DOI":"10.3390\/w12040985"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"931","DOI":"10.3390\/rs11080931","article-title":"Shallow landslide prediction using a novel hybrid functional machine learning algorithm","volume":"11","author":"Bui","year":"2019","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.catena.2019.03.017","article-title":"Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution","volume":"178","author":"Shirzadi","year":"2019","journal-title":"Catena"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.catena.2013.11.014","article-title":"Landslide susceptibility mapping at central zab basin, iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models","volume":"115","author":"Shahabi","year":"2014","journal-title":"Catena"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Mohammadi, A., Shahabi, H., Ahmad, B.B., Al-Ansari, N., Shirzadi, A., Clague, J.J., Jaafari, A., Chen, W., and Nguyen, H. (2020). Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17144933"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Shirzadi, A., Shahabi, H., Singh, S.K., Al-Ansari, N., Clague, J.J., Jaafari, A., Chen, W., Miraki, S., and Dou, J. (2020). Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, na\u00efve bayes tree, artificial neural network, and support vector machine algorithms. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17082749"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1080\/10106049.2018.1499820","article-title":"A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment","volume":"34","author":"Abedini","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nhu, V.H., Janizadeh, S., Avand, M., Chen, W., Farzin, M., Omidvar, E., Shirzadi, A., Shahabi, H., Clague, J.J., and Jaafari, A. (2020). GIS-based gully erosion susceptibility mapping: A comparison of computational ensemble data mining models. Appl. Sci., 10.","DOI":"10.3390\/app10062039"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1002\/esp.404","article-title":"Impact of road building on gully erosion risk: A case study from the northern ethiopian highlands","volume":"27","author":"Nyssen","year":"2010","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_30","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_31","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_32","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_33","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.scitotenv.2017.07.198","article-title":"Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling","volume":"609","author":"Pourghasemi","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1002\/ldr.2772","article-title":"Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity","volume":"29","author":"Conoscenti","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Saha, S., Roy, J., Arabameri, A., Blaschke, T., and Bui, D.T. (2020). Machine learning-based gully erosion susceptibility mapping: A case study of eastern india. Sensors, 20.","DOI":"10.3390\/s20051313"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"104223","DOI":"10.1016\/j.catena.2019.104223","article-title":"Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling","volume":"183","author":"Arabameri","year":"2019","journal-title":"Catena"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Avand, M., 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_40","doi-asserted-by":"crossref","unstructured":"Pourghasemi, H.R., Gayen, A., Haque, S.M., and Bai, S. (2020). Gully erosion susceptibility assessment through the svm machine learning algorithm (svm-mla). Gully Erosion Studies from India and Surrounding Regions, Springer.","DOI":"10.1007\/978-3-030-23243-6_28"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Choubin, B., Rahmati, O., Tahmasebipour, N., Feizizadeh, B., and Pourghasemi, H.R. (2019). Application of fuzzy analytical network process model for analyzing the gully erosion susceptibility. Natural Hazards Gis-Based Spatial Modeling Using Data Mining Techniques, Springer.","DOI":"10.1007\/978-3-319-73383-8_5"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shit, P.K., Bhunia, G.S., and Pourghasemi, H.R. (2020). Gully erosion susceptibility mapping based on bayesian weight of evidence. Gully Erosion Studies from India and Surrounding Regions, Springer.","DOI":"10.1007\/978-3-030-23243-6"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Frankl, A., Guyassa, E., Poesen, J., and Nyssen, J. (2019). Gully erosion and control in the tembien highlands. Geo-Trekking in Ethiopia\u2019s Tropical Mountains, Springer.","DOI":"10.1007\/978-3-030-04955-3_22"},{"key":"ref_44","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 ve-bayes tree, and alternating decision tree models","volume":"8","author":"Chen","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2018\/v11i12\/99745","article-title":"Machine learning methods of kernel logistic regression and classification and regression trees for landslide susceptibility assessment at part of himalayan area, India","volume":"11","author":"Pham","year":"2018","journal-title":"Indian J. Sci. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, G., Lei, X., Chen, W., Shahabi, H., and Shirzadi, A. (2020). Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry, 12.","DOI":"10.3390\/sym12030325"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Phong, T.V., Nguyen, H.D., Qi, C., Al-Ansari, N., Amini, A., Ho, L.S., Tuyen, T.T., Yen, H.P.H., and Ly, H.-B. (2020). A comparative study of kernel logistic regression, radial basis function classifier, multinomial na ve bayes, and logistic model tree for flash flood susceptibility mapping. Water, 12.","DOI":"10.3390\/w12010239"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Ha, D.H., Nguyen, H.D., Van Phong, T., Trinh, P.T., Al-Ansari, N., Le, H.V., Pham, B.T., Ho, L.S., and Prakash, I. (2020). Improvement of credal decision trees using ensemble frameworks for groundwater potential modeling. Sustainability, 12.","DOI":"10.3390\/su12072622"},{"key":"ref_49","first-page":"45","article-title":"Investigation and comparison of gully erosion characteristics in agricultural and rangeland land use, case study: Robat turk watershed","volume":"4","author":"Shadfar","year":"2012","journal-title":"J. Watershed Manag. Eng."},{"key":"ref_50","first-page":"15","article-title":"Multi parametric gis analysis to assess gully erosion susceptibility: A test in southern sicily, Italy","volume":"17","author":"Agnesi","year":"2011","journal-title":"Landf. Anal."},{"key":"ref_51","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_52","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_53","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_54","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Chen, W., Lombardo, L., Blaschke, T., and Bui, D.T. (2020). Hybrid computational intelligence models for improvement gully erosion assessment. Remote Sens., 12.","DOI":"10.3390\/rs12010140"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s11069-015-1703-0","article-title":"Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: Advantages and limitations","volume":"79","author":"Conoscenti","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1007\/s11769-014-0663-8","article-title":"Extraction and analysis of gully head of loess plateau in china based on digital elevation model","volume":"24","author":"Zhu","year":"2014","journal-title":"Chin. Geogr. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"8075","DOI":"10.15666\/aeer\/1606_80758091","article-title":"Integration of insartechnique, google earth images and extensive field survey for landslide inventory in a part of cameron highlands, pahang, malaysia","volume":"16","author":"Mohammadi","year":"2007","journal-title":"Appl. Ecol. Environ. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s13762-013-0464-0","article-title":"Gis-based frequency ratio and index of entropy models for landslide susceptibility assessment in the caspian forest, northern iran","volume":"11","author":"Jaafari","year":"2014","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_59","first-page":"45","article-title":"Floristic differentiation in limestone outcrops of southern mexico and central brazil: A beta diversity approach","volume":"84","author":"Sevilha","year":"2019","journal-title":"Bot. Sci."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.geomorph.2011.07.006","article-title":"Comparison of gis-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern calabria, south italy","volume":"134","author":"Conforti","year":"2011","journal-title":"Geomorphology"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geomorph.2017.09.007","article-title":"Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques","volume":"297","author":"Chen","year":"2017","journal-title":"Geomorphology"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s00704-016-2022-4","article-title":"A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in iran using r and gis","volume":"131","author":"Naghibi","year":"2018","journal-title":"Theor. Appl. Climatol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"124602","DOI":"10.1016\/j.jhydrol.2020.124602","article-title":"Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping","volume":"583","author":"Chen","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"4397","DOI":"10.1007\/s10064-018-1401-8","article-title":"Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling","volume":"78","author":"Chen","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_67","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_68","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":"2014","journal-title":"J. Hydrol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1007\/s12517-012-0825-x","article-title":"Gis-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (north of tehran, Iran)","volume":"7","author":"Pourghasemi","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s12517-012-0795-z","article-title":"Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and gis","volume":"7","author":"Manap","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Poesen, J. (1993). Gully typology and gully control measures in the european loess belt. Farm Land Erosion in Temperate Plains Environments and Hills, Elsevier.","DOI":"10.1016\/B978-0-444-81466-1.50024-1"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1002\/esp.423","article-title":"Road construction and gully erosion in west pokot, kenya","volume":"27","author":"Jungerius","year":"2002","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_73","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_74","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s002540000163","article-title":"Assessment of landslide susceptibility on the natural terrain of lantau island, Hong Kong","volume":"40","author":"Dai","year":"2001","journal-title":"Environ. Geol."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Guo, C., Qin, Y., Ma, D., Xia, Y., Chen, Y., Si, Q., and Lu, L. (2019). Ionic composition, geological signature and environmental impacts of coalbed methane produced water in China. Energy Sources Part A Recovery Util. Environ. Eff., 1\u201315.","DOI":"10.1080\/15567036.2019.1636161"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1007\/s10064-019-01609-9","article-title":"A new slice-based method for calculating the minimum safe thickness for a filled-type karst cave","volume":"79","author":"Xu","year":"2020","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"103033","DOI":"10.1016\/j.tust.2019.103033","article-title":"An interval risk assessment method and management of water inflow and inrush in course of karst tunnel excavation","volume":"92","author":"Wang","year":"2019","journal-title":"Tunnel. Undergr. Space Technol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.1007\/s10064-018-1294-6","article-title":"Risk assessment of water inrush in karst tunnels excavation based on normal cloud model","volume":"78","author":"Wang","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"105310","DOI":"10.1016\/j.enggeo.2019.105310","article-title":"A deterministic-stochastic identification and modelling method of discrete fracture networks using laser scanning: Development and case study","volume":"262","author":"Pan","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1002\/hyp.11163","article-title":"Cokriging for enhanced spatial interpolation of rainfall in two australian catchments","volume":"31","author":"Adhikary","year":"2017","journal-title":"Hydrol. Process."},{"key":"ref_81","first-page":"3","article-title":"Creating singapore\u2019s longest monthly rainfall record from 1839 to the present","volume":"1","author":"Gao","year":"2018","journal-title":"MSS Res. Lett."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2898","DOI":"10.1002\/joc.4180","article-title":"A comparison among spatial interpolation techniques for daily rainfall data in sichuan province, china","volume":"35","author":"Xu","year":"2015","journal-title":"Int. J. Climatol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.jhydrol.2016.06.027","article-title":"Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using gis","volume":"540","author":"Bui","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_84","unstructured":"Hair, J., Anderson, R., Tatham, R., and Black, W. (2009). Multivariate Data Analysis, Prentice Hall."},{"key":"ref_85","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_86","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.catena.2018.01.012","article-title":"Gis-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method","volume":"164","author":"Chen","year":"2018","journal-title":"Catena"},{"key":"ref_87","unstructured":"Sewell, M. (2009). Kernel Methods, Department of Computer Science, University College London."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer Series in Statistics.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Karsmakers, P., Pelckmans, K., and Suykens, J.A. (2007, January 12\u201317). Multi-class kernel logistic regression: A fixed-size implementation. Proceedings of the International Joint Conference on Neural Networks, Orlando, FL, USA.","DOI":"10.1109\/IJCNN.2007.4371223"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1198\/106186005X25619","article-title":"Kernel logistic regression and the import vector machine","volume":"14","author":"Zhu","year":"2005","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.csda.2010.06.014","article-title":"Robust weighted kernel logistic regression in imbalanced and rare events data","volume":"55","author":"Maalouf","year":"2011","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10287-010-0128-1","article-title":"Kernel logistic regression using truncated newton method","volume":"8","author":"Maalouf","year":"2011","journal-title":"Comput. Manag. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"4625","DOI":"10.1016\/j.eswa.2014.01.017","article-title":"Credal-c4. 5: Decision tree based on imprecise probabilities to classify noisy data","volume":"41","author":"Mantas","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.ijar.2004.10.001","article-title":"Upper entropy of credal sets. Applications to credal classification","volume":"39","author":"Abellan","year":"2005","journal-title":"Int. J. Approx. Reason."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1142\/S0218488509006297","article-title":"A filter-wrapper method to select variables for the naive bayes classifier based on credal decision trees","volume":"17","author":"Abellan","year":"2009","journal-title":"Int. J. Uncertain. Fuzziness Knowl. Based Syst."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Mantas, C.J., and Abellan, J. (2014, January 22\u201324). Credal decision trees to classify noisy data sets. Proceedings of the International Conference on Hybrid Artificial Intelligence Systems, Bilbao, Spain.","DOI":"10.1007\/978-3-319-07617-1_60"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Abellan, J., and Masegosa, A.R. (2010). Bagging decision trees on data sets with classification noise. International Symposium on Foundations of Information and Knowledge Systems, Springer.","DOI":"10.1007\/978-3-642-11829-6_17"},{"key":"ref_98","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees Regression Trees, Chapman and Hall\/CRC. Wadsworth, Belmont."},{"key":"ref_99","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_100","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Jarihani, B., Piralilou, S.T., Chittleborough, D., Avand, M., and Ghorbanzadeh, O. (2019). A semi-automated object-based gully networks detection using different machine learning models: A case study of bowen catchment, queensland, australia. Sensors, 19.","DOI":"10.3390\/s19224893"},{"key":"ref_101","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_102","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1021\/ci034160g","article-title":"Random forest:\u2009 A classification and regression tool for compound classification and qsar modeling","volume":"43","author":"Svetnik","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.csda.2006.12.030","article-title":"Unbiased split selection for classification trees based on the gini index","volume":"52","author":"Strobl","year":"2007","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1023\/B:MACH.0000027782.67192.13","article-title":"Functional trees","volume":"55","author":"Gama","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_107","first-page":"155","article-title":"Comparative analysis of data mining classification algorithms in type-2 diabetes prediction data using weka approach","volume":"7","author":"Ahmed","year":"2014","journal-title":"J. Life Support Eng."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Costache, R., Pham, Q.B., Sharifi, E., Linh, N.T.T., Abba, S.I., Vojtek, M., Vojtekov\u00e1, J., Nhi, P.T.T., and Khoi, D.N. (2020). Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and gis techniques. Remote Sens., 12.","DOI":"10.3390\/rs12010106"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1080\/19475705.2017.1401560","article-title":"A novel hybrid artificial intelligence approach based on the rotation forest ensemble and na\u00efve bayes tree classifiers for a landslide susceptibility assessment in langao county, china","volume":"8","author":"Chen","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Lei, X., Chen, W., and Pham, B.T. (2020). Performance evaluation of gis-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS Int. J. Geo Inf., 9.","DOI":"10.3390\/ijgi9070443"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.3390\/rs10101527","article-title":"Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in cameron highlands, malaysia","volume":"10","author":"Bui","year":"2018","journal-title":"Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Shirzadi, A., Shahabi, H., Chen, W., Clague, J.J., Geertsema, M., Jaafari, A., Avand, M., Miraki, S., and Talebpour Asl, D. (2020). Shallow landslide susceptibility mapping by random forest base classifier and its ensembles in a semi-arid region of Iran. Forests, 11.","DOI":"10.3390\/f11040421"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.jhydrol.2017.03.020","article-title":"A comparative assessment of gis-based data mining models and a novel ensemble model in groundwater well potential mapping","volume":"548","author":"Naghibi","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Chen, W., Fan, L., Li, C., and Pham, B.T. (2020). Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in nanzheng county, China. Appl. Sci., 10.","DOI":"10.3390\/app10010029"},{"key":"ref_115","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_116","doi-asserted-by":"crossref","first-page":"104777","DOI":"10.1016\/j.catena.2020.104777","article-title":"Gis-based evaluation of landslide susceptibility using hybrid computational intelligence models","volume":"195","author":"Chen","year":"2020","journal-title":"Catena"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2020). Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens., 12.","DOI":"10.3390\/rs12142180"},{"key":"ref_118","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_119","doi-asserted-by":"crossref","unstructured":"Avand, M., Janizadeh, S., Bui, D.T., Pham, V.H., Ngo, P.T.T., and Nhu, V.-H. (2020). A tree-based intelligence ensemble approach for spatial prediction of potential groundwater. Int. J. Digit. Earth, 1\u201322.","DOI":"10.1080\/17538947.2020.1718785"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-33755-7","article-title":"Novel hybrid evolutionary algorithms for spatial prediction of floods","volume":"8","author":"Bui","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.3390\/w10091210","article-title":"New hybrids of anfis with several optimization algorithms for flood susceptibility modeling","volume":"10","author":"Bui","year":"2018","journal-title":"Water"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Li, Y., and Chen, W. (2020). Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water, 12.","DOI":"10.3390\/w12010113"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"134514","DOI":"10.1016\/j.scitotenv.2019.134514","article-title":"Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models","volume":"711","author":"Costache","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1080\/11263500903429213","article-title":"Effects of fragmentation on vascular plant diversity in a mediterranean forest archipelago","volume":"144","author":"Rosati","year":"2010","journal-title":"Plant Biosyst."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geoderma.2019.01.050","article-title":"Gully headcut susceptibility modeling using functional trees, na\u00efve bayes tree, and random forest models","volume":"342","author":"Hosseinalizadeh","year":"2019","journal-title":"Geoderma"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.geomorph.2016.05.009","article-title":"Timing and causes of gully erosion in the riparian zone of the semi-arid tropical victoria river, australia: Management implications","volume":"266","author":"McCloskey","year":"2016","journal-title":"Geomorphology"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/S0341-8162(02)00132-7","article-title":"Medium-term gully headcut retreat rates in southeast spain determined from aerial photographs and ground measurements","volume":"50","author":"Vandekerckhove","year":"2003","journal-title":"Catena"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.geomorph.2016.04.021","article-title":"Degradation of the mitchell river fluvial megafan by alluvial gully erosion increased by post-european land use change, queensland, australia","volume":"266","author":"Shellberg","year":"2016","journal-title":"Geomorphology"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.earscirev.2016.01.009","article-title":"How fast do gully headcuts retreat?","volume":"154","author":"Vanmaercke","year":"2016","journal-title":"Earth Sci. Rev."},{"key":"ref_130","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_131","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.catena.2018.10.036","article-title":"Soil resistance to flowing water erosion of seven typical plant communities on steep gully slopes on the loess plateau of China","volume":"173","author":"Zhang","year":"2019","journal-title":"Catena"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geomorph.2015.06.001","article-title":"Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in giampilieri (ne sicily, Italy)","volume":"249","author":"Trigila","year":"2015","journal-title":"Geomorphology"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.ecolind.2014.12.028","article-title":"A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an afromontane landscape","volume":"52","author":"Were","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1016\/j.scitotenv.2018.06.389","article-title":"Performance evaluation of the gis-based data mining techniques of best-first decision tree, random forest, and na\u00efve bayes tree for landslide susceptibility modeling","volume":"644","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.catena.2018.02.012","article-title":"Impact of piping on gully development in mid-altitude mountains under a temperate climate: A dendrogeomorphological approach","volume":"165","year":"2018","journal-title":"Catena"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.geomorph.2010.02.001","article-title":"Factors controlling the spatial distribution of soil piping erosion on loess-derived soils: A case study from central belgium","volume":"118","author":"Verachtert","year":"2010","journal-title":"Geomorphology"},{"key":"ref_137","unstructured":"Bull, L.J., and Kirkby, M.J. (2002). Gully erosion in dryland environments. Dryland Rivers: Hydrology and Geomorphology of Semi-Arid Channels, Wiley."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.scitotenv.2016.10.025","article-title":"Suitability estimation for urban development using multi-hazard assessment map","volume":"575","author":"Bathrellos","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Yanar, T., Kocaman, S., and Gokceoglu, C. (2020). Use of mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (mamak, ankara, turkey). ISPRS Int. J. Geo Inf., 9.","DOI":"10.3390\/ijgi9020114"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s12665-018-8003-4","article-title":"Multi-hazard assessment modeling via multi-criteria analysis and gis: A case study","volume":"78","author":"Skilodimou","year":"2019","journal-title":"Environ. Earth Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2478\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:53:46Z","timestamp":1760176426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/15\/2478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,2]]},"references-count":140,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12152478"],"URL":"https:\/\/doi.org\/10.3390\/rs12152478","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,2]]}}}