{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T15:49:04Z","timestamp":1783352944217,"version":"3.54.6"},"reference-count":90,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use\/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.<\/jats:p>","DOI":"10.3390\/rs12172742","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T09:24:56Z","timestamp":1598347496000},"page":"2742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7263-6209","authenticated-orcid":false,"given":"Ehsan","family":"Kamali Maskooni","sequence":"first","affiliation":[{"name":"Division of Water Resources Engineering and Centre for Middle Eastern Studies, Lund University, Box 118, SE 221 00 Lund, Sweden"},{"name":"Department of Watershed Management and Engineering, University of Hormozgan, Bandar Abbas 79161-93145, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1449-8933","authenticated-orcid":false,"given":"Seyed Amir","family":"Naghibi","sequence":"additional","affiliation":[{"name":"Division of Water Resources Engineering and Centre for Middle Eastern Studies, Lund University, Box 118, SE 221 00 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2160-1772","authenticated-orcid":false,"given":"Hossein","family":"Hashemi","sequence":"additional","affiliation":[{"name":"Division of Water Resources Engineering and Centre for Middle Eastern Studies, Lund University, Box 118, SE 221 00 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1473-0138","authenticated-orcid":false,"given":"Ronny","family":"Berndtsson","sequence":"additional","affiliation":[{"name":"Division of Water Resources Engineering and Centre for Middle Eastern Studies, Lund University, Box 118, SE 221 00 Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wada, Y., Van Beek, L.P.H., Van Kempen, C.M., Reckman, J.W.T.M., Vasak, S., and Bierkens, M.F.P. (2010). Global depletion of groundwater resources. Geophys. Res. Lett., 37.","DOI":"10.1029\/2010GL044571"},{"key":"ref_2","unstructured":"Alcamo, J., Henrich, T., and Rosch, T. (2000). World Water in 2025\u2014Global Modelling and Scenario Analysis for the World Commission on Water for the 21st Century, Centre for Environmental System Research, University of Kassel. Report A0002."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1080\/10106049.2015.1073366","article-title":"Assessment of a spatial multi-criteria evaluation to site selection underground dams in the Alborz Province, Iran","volume":"31","author":"Chezgi","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4399","DOI":"10.1007\/s11269-017-1754-y","article-title":"Appraising the accuracy of multi-class frequency ratio and weights of evidence method for delineation of regional groundwater potential zones in canal command system","volume":"31","author":"Sahoo","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10040-016-1466-z","article-title":"Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features","volume":"25","author":"Naghibi","year":"2017","journal-title":"Hydrogeol. J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kim, J.C., Jung, H.S., and Lee, S. (2019). Spatial mapping of the groundwater potential of the Geum River basin using ensemble models based on remote sensing images. Remote Sens., 11.","DOI":"10.3390\/rs11192285"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Moghaddam, D.D., Rahmati, O., Haghizadeh, A., and Kalantari, Z. (2020). A modeling comparison of groundwater potential mapping in a mountain bedrock aquifer: QUEST, GARP, and RF models. Water, 12.","DOI":"10.3390\/w12030679"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Al-Najjar, H.A.H., Pradhan, B., Saeidi, V., Halin, A.A., Ueda, N., and Naghibi, S.A. (2019). Optimized conditioning factors using machine learning techniques for groundwater potential mapping. Water, 11.","DOI":"10.3390\/w11091909"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"125197","DOI":"10.1016\/j.jhydrol.2020.125197","article-title":"Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors","volume":"589","author":"Naghibi","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.geomorph.2008.03.015","article-title":"Weight of evidence and artificial neural networks for potential groundwater spring mapping: An application to the Mt. Modino area (Northern Apennines, Italy)","volume":"111","author":"Corsini","year":"2009","journal-title":"Geomorphology"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lee, S., Hyun, Y., Lee, S., and Lee, M.-J. (2020). Groundwater potential mapping using remote sensing and GIS-based machine learning techniques. Remote Sens., 12.","DOI":"10.3390\/rs12071200"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Al-Djazouli, M.O., Elmorabiti, K., Rahimi, A., Amellah, O., and Fadil, O.A.M. (2020). Delineating of groundwater potential zones based on remote sensing, GIS and analytical hierarchical process: A case of Waddai, eastern Chad. GeoJournal, 1\u201314.","DOI":"10.1007\/s10708-020-10160-0"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1111\/gwat.12939","article-title":"Mapping groundwater potential through an ensemble of big data methods","volume":"58","author":"Renard","year":"2020","journal-title":"Groundwater"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.jhydrol.2019.03.013","article-title":"Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization","volume":"572","author":"Chen","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104421","DOI":"10.1016\/j.catena.2019.104421","article-title":"The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers","volume":"187","author":"Moghaddam","year":"2020","journal-title":"CATENA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-019-7362-y","article-title":"Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential","volume":"191","author":"Naghibi","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jhydrol.2011.05.015","article-title":"Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)","volume":"405","author":"Ozdemir","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.jhydrol.2011.10.010","article-title":"GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison","volume":"411","author":"Ozdemir","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Nguyen, P.T., Ha, D.H., Avand, M., Jaafari, A., Nguyen, H.D., Al-Ansari, N., Van Phong, T., Sharma, R., Kumar, R., and Van Le, H. (2020). Soft computing ensemble models based on logistic regression for groundwater potential mapping. Appl. Sci., 10.","DOI":"10.3390\/app10072469"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.2166\/hydro.2018.120","article-title":"Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: Focus on topographic factors","volume":"20","author":"Kim","year":"2018","journal-title":"J. Hydroinformatics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.envsoft.2017.06.012","article-title":"A novel hybrid artificial intelligence approach for flood susceptibility assessment","volume":"95","author":"Chapi","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"CATENA"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/10106049.2015.1128486","article-title":"The use of logistic model tree (LMT) for pixel and object based classifications using high resolution WorldView 2 imagery","volume":"32","author":"Colkesen","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_28","unstructured":"Cortes, C., Mohri, M., and Syed, U. (2014, January 21\u201326). Deep Boosting. Proceedings of the 31st International Conference on International Conference on Machine Learning, Beijing, China."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Van Phong, T., 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\u00efve bayes, and logistic model tree for flash flood susceptibility mapping. Water, 12.","DOI":"10.3390\/w12010239"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Khosravi, K., Melesse, A.M., Shahabi, H., Shirzadi, A., Chapi, K., and Hong, H. (2019). Flood susceptibility mapping at Ningdu catchment, China using bivariate and data mining techniques. Extreme Hydrology and Climate Variability, Elsevier.","DOI":"10.1016\/B978-0-12-815998-9.00033-6"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s40808-016-0174-y","article-title":"Modeling groundwater probability index in Ponnaiyar River basin of South India using analytic hierarchy process","volume":"2","author":"Jothibasu","year":"2016","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1111\/j.1467-8306.1985.tb00061.x","article-title":"Landslide susceptibility mapping in the Amahata River Basin, Japan","volume":"75","author":"Aniya","year":"1985","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2012.03.003","article-title":"Application of an evidential belief function model in landslide susceptibility mapping","volume":"44","author":"Althuwaynee","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.catena.2013.10.011","article-title":"A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping","volume":"114","author":"Althuwaynee","year":"2014","journal-title":"CATENA"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s12524-011-0175-2","article-title":"Mapping and assessment of groundwater potential in Bilrai watershed (Shivpuri District, M.P.) a geomatics approach","volume":"40","author":"Sinha","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Benjmel, K., Amraoui, F., Boutaleb, S., Ouchchen, M., Tahiri, A., and Touab, A. (2020). Mapping of groundwater potential zones in crystalline terrain using remote sensing, GIS techniques, and multicriteria data analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco). Water, 12.","DOI":"10.3390\/w12020471"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1007\/s12517-014-1391-1","article-title":"Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster\u2013Shafer model","volume":"8","author":"Mogaji","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Razavi-Termeh, S.V., Sadeghi-Niaraki, A., and Choi, S.M. (2019). Groundwater potential mapping using an integrated ensemble of three bivariate statistical models with random forest and logistic model tree models. Water, 11.","DOI":"10.3390\/w11081596"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s11053-017-9367-y","article-title":"Analyzing factors of groundwater potential and its relation with population in the Lower Barpani Watershed, Assam, India","volume":"27","author":"Ahmed","year":"2018","journal-title":"Nat. Resour. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-015-5049-6","article-title":"GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran","volume":"188","author":"Naghibi","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1029\/WR022i008p01350","article-title":"Sediment transport capacity of sheet and rill flow: Application of unit stream power theory","volume":"22","author":"Moore","year":"1986","journal-title":"Water Resour. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s40899-016-0056-5","article-title":"A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra\u2013Al Al-Gharbi\u2013Teeb areas, Iraq","volume":"2","year":"2016","journal-title":"Sustain. Water Resour. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Choubin, B., Rahmati, O., Soleimani, F., Alilou, H., Moradi, E., and Alamdari, N. (2019). Regional groundwater potential analysis using classification and regression trees. Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier.","DOI":"10.1016\/B978-0-12-815226-3.00022-3"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1029\/TR013i001p00350","article-title":"Drainage-basin characteristics","volume":"13","author":"Horton","year":"1932","journal-title":"Trans. Am. Geophys. Union"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1029\/97WR02709","article-title":"On the sensitivity of drainage density to climate change","volume":"34","author":"Moglen","year":"1998","journal-title":"Water Resour. Res."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.scitotenv.2017.12.121","article-title":"A cost-effective and efficient framework to determine water quality monitoring network locations","volume":"624","author":"Alilou","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11069-012-0347-6","article-title":"Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling\u2013Narayanghat road section in Nepal Himalaya","volume":"65","author":"Devkota","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"595","DOI":"10.2166\/aqua.2019.159","article-title":"Groundwater prospect mapping using remote sensing, GIS and resistivity survey techniques in Chhokra Nala Raipur district, Chhattisgarh, India","volume":"68","author":"Indhulekha","year":"2019","journal-title":"J. Water Supply Res. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101846","DOI":"10.1016\/j.scs.2019.101846","article-title":"Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000\u20132018 over a sub-tropical Indian City","volume":"52","author":"Sultana","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Dissanayake, D., Morimoto, T., Ranagalage, M., and Murayama, Y. (2019). Land-use\/land-cover changes and their impact on surface urban heat islands: Case study of Kandy City, Sri Lanka. Climate, 7.","DOI":"10.3390\/cli7080099"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1007\/s10661-013-3491-x","article-title":"GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia","volume":"186","author":"Nigatu","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2006.09.003","article-title":"Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery","volume":"106","author":"Yuan","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/BF02990737","article-title":"Application of lineament density and hydrogeomorphology to delineate groundwater potential zones of Baghmundi block in Purulia District, West Bengal","volume":"33","author":"Nag","year":"2005","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/s12594-012-0198-5","article-title":"Hydraulic significance of fracture correlated lineaments in precambrian rocks in Purulia district, West Bengal","volume":"80","author":"Acharya","year":"2012","journal-title":"J. Geol. Soc. India"},{"key":"ref_58","first-page":"1","article-title":"Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models","volume":"32","author":"Falah","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_59","unstructured":"Geology Survey of Iran (GSI) (2020, July 20). Geological Survey and Mineral Exploration of Iran, Available online: http:\/\/wwwgsiir\/Main\/Lang_en\/indexhtml."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.scitotenv.2018.01.266","article-title":"A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran","volume":"627","author":"Khosravi","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0020-7373(87)80053-6","article-title":"Simplifying decision trees","volume":"27","author":"Quinlan","year":"1987","journal-title":"Int. J. Man. Mach. Stud."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial prediction models for shallow landslide hazards: A comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree","volume":"13","author":"Tuan","year":"2016","journal-title":"Landslides"},{"key":"ref_63","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, Wadsworth and Brooks\/Cole."},{"key":"ref_64","unstructured":"Kuhn, M., Wing, J., Weston, S., Andre, W., Chris, K., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., and Team, R.C. (2020, March 15). Classification and Regression Training. Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/caret.pdf."},{"key":"ref_65","unstructured":"Hornik, K., Buchta, C., Hothorn, T., Karatzoglou, A., Meyer, D., and Zeileis, A. (2020, March 15). R\/Weka Interface. Available online: https:\/\/cran.r-project.org\/web\/packages\/RWeka\/RWeka.pdf."},{"key":"ref_66","unstructured":"Marcous, D., and Sandbank, Y. (2020, March 15). Deep Boosting Ensemble Modeling. Available online: https:\/\/cran.r-project.org\/web\/packages\/deepboost\/deepboost.pdf."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2016","journal-title":"Landslides"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"91","DOI":"10.3934\/geosci.2017.1.91","article-title":"GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran","volume":"3","author":"Mousavi","year":"2017","journal-title":"AIMS Geosci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2015.08.001","article-title":"Representing conditional preference by boosted regression trees for recommendation","volume":"327","author":"Liu","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1177\/1536867X0500500304","article-title":"Boosted Regression (Boosting): An Introductory Tutorial and a Stata Plugin","volume":"5","author":"Schonlau","year":"2005","journal-title":"Stata J. Promot. Commun. Stat. Stata"},{"key":"ref_72","unstructured":"Greenwell, B., Boehmke, B., and Cunningham, J. (2020, March 15). Generalized Boosted Regression Models. Available online: https:\/\/cran.r-project.org\/web\/packages\/gbm\/gbm.pdf."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1016\/j.jclepro.2019.04.293","article-title":"Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater","volume":"228","author":"Motevalli","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","unstructured":"Avand, M., Janizadeh, S., Naghibi, S.A., Pourghasemi, H.R., Khosrobeigi Bozchaloei, S., and Blaschke, T. (2019). A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water, 11.","DOI":"10.3390\/w11102076"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TSM.2007.907607","article-title":"Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes","volume":"20","author":"He","year":"2007","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4171","DOI":"10.1007\/s10661-012-2859-7","article-title":"Predicting copper concentrations in acid mine drainage: A comparative analysis of five machine learning techniques","volume":"185","author":"Betrie","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_78","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_79","unstructured":"Liaw, A., and Wiener, M. (2020, March 15). Breiman and Cutler\u2019s Random Forests for Classification and Regression. Available online: https:\/\/cran.r-project.org\/web\/packages\/randomForest\/randomForest.pdf."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s12517-015-2258-9","article-title":"Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran","volume":"9","author":"Sangchini","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10661-018-6507-8","article-title":"Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS","volume":"190","author":"Golkarian","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Naghibi, S., Vafakhah, M., Hashemi, H., Pradhan, B., and Alavi, S. (2018). Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad Plain, Iran). Water, 10.","DOI":"10.3390\/w10101405"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"100610","DOI":"10.1016\/j.ejrh.2019.100610","article-title":"Groundwater potential assessment using GIS and remote sensing: A case study of Guna tana landscape, upper blue Nile Basin, Ethiopia","volume":"24","author":"Andualem","year":"2019","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.enggeo.2005.02.002","article-title":"Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)","volume":"79","author":"Yesilnacar","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11269-017-1660-3","article-title":"Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping","volume":"31","author":"Naghibi","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1016\/j.compbiomed.2012.06.011","article-title":"HIV-1 CRF01_AE coreceptor usage prediction using kernel methods based logistic model trees","volume":"42","author":"Shoombuatong","year":"2012","journal-title":"Comput. Biol. Med."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Chen, W., Loche, M., Zhao, X., Li, Y., Lombardo, L., Cerda, A., Pradhan, B., and Bui, D.T. (2019). Comparison of machine learning models for gully erosion susceptibility mapping. Geosci. Front.","DOI":"10.1016\/j.gsf.2019.11.009"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1007\/s12665-020-08944-1","article-title":"A comparison of machine learning models for the mapping of groundwater spring potential","volume":"79","author":"Pourghasemi","year":"2020","journal-title":"Environ. Earth Sci."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Caruana, R., and Niculescu-Mizil, A. (2006, January 25\u201329). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning\u2014ICML \u201906, Pittsburgh, Pennsylvania.","DOI":"10.1145\/1143844.1143865"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1007\/s11053-019-09530-4","article-title":"Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms","volume":"29","author":"Naghibi","year":"2020","journal-title":"Nat. Resour. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2742\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:03Z","timestamp":1760177163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,24]]},"references-count":90,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12172742"],"URL":"https:\/\/doi.org\/10.3390\/rs12172742","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,24]]}}}