{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T16:59:29Z","timestamp":1782665969503,"version":"3.54.5"},"reference-count":97,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,14]],"date-time":"2019-12-14T00:00:00Z","timestamp":1576281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund","doi-asserted-by":"publisher","award":["DK W 1237-N23"],"award-info":[{"award-number":["DK W 1237-N23"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Groundwater is one of the most important natural resources, as it regulates the earth\u2019s hydrological system. The Damghan sedimentary plain area, located in the region of a semi-arid climate of Iran, has very critical conditions of groundwater due to massive pressure on it and is in need of robust models for identifying the groundwater potential zones (GWPZ). The main goal of the current research is to prepare a groundwater potentiality map (GWPM) considering the probabilistic, machine learning, data mining, and multi-criteria decision analysis (MCDA) approaches. For this purpose, 80 wells collected from the Iranian groundwater resource department and field investigation with global positioning system (GPS), have been selected randomly and considered as the groundwater inventory datasets. Out of 80 wells, 56 (70%) wells have been brought into play for modeling and 24 (30%) for validation purposes. Elevation, slope, aspect, convergence index (CI), rainfall, drainage density (Dd), distance to river, distance to fault, distance to road, lithology, soil type, land use\/land cover (LU\/LC), normalized difference vegetation index (NDVI), topographic wetness index (TWI), topographic position index (TPI), and stream power index (SPI) have been used for modeling purpose. The area under the receiver operating characteristic (AUROC), sensitivity (SE), specificity (SP), accuracy (AC), mean absolute error (MAE), and root mean square error (RMSE) are used for checking the goodness-of-fit and prediction accuracy of approaches to compare their performance. In addition, the influence of groundwater determining factors (GWDFs) on groundwater occurrence was evaluated by performing a sensitivity analysis model. The GWPMs, produced by technique for order preference by similarity to ideal solution (TOPSIS), random forest (RF), binary logistic regression (BLR), weight of evidence (WoE) and support vector machine (SVM) have been classified into four categories, i.e., low, medium, high and very high groundwater potentiality with the help of the natural break classification methods in the GIS environment. The very high groundwater potentiality class is covered 15.09% for TOPSIS, 15.46% for WoE, 25.26% for RF, 15.47% for BLR, and 18.74% for SVM of the entire plain area. Based on sensitivity analysis, distance from river, and drainage density represent significantly effects on the groundwater occurrence. validation results show that the BLR model with best prediction accuracy and goodness-of-fit outperforms the other five models. Although, all models have very good performance in modeling of groundwater potential. Results of seed cell area index model that used for checking accuracy classification of models show that all models have suitable performance. Therefore, these are promising models that can be applied for the GWPZs identification, which will help for some needful action of these areas.<\/jats:p>","DOI":"10.3390\/rs11243015","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:19:38Z","timestamp":1576473578000},"page":"3015","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Application of Probabilistic and Machine Learning Models for Groundwater Potentiality Mapping in Damghan Sedimentary Plain, Iran"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1142-1666","authenticated-orcid":false,"given":"Alireza","family":"Arabameri","sequence":"first","affiliation":[{"name":"Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagabandhu","family":"Roy","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Gour Banga, Malda, West Bengal 732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2739-3716","authenticated-orcid":false,"given":"Sunil","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Gour Banga, Malda, West Bengal 732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1860-8458","authenticated-orcid":false,"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics \u2013 Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9664-8770","authenticated-orcid":false,"given":"Omid","family":"Ghorbanzadeh","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics \u2013 Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-6479","authenticated-orcid":false,"given":"Dieu","family":"Tien Bui","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Berhanu, B., Seleshi, Y., and Melesse, A.M. (2014). Surface Water and Groundwater Resources of Ethiopia: Potentials and Challenges of Water Resources Development, Springer.","DOI":"10.1007\/978-3-319-02720-3_6"},{"key":"ref_2","unstructured":"Graciela, S.M., and Courel, M.F. (2001). High demand in a land of water scarcity: Iran. Water and Sustainability in Arid Regions, Springer. [1st ed.]."},{"key":"ref_3","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":"2012","journal-title":"Arab. J. Geosci."},{"key":"ref_4","unstructured":"National Geography Society (2005). National Geographic, Almanac of Geography, National Geography Society. National Geographic Books."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s11269-008-9272-6","article-title":"Cost-effective approaches for sustainable groundwater management in alluvial aquifer systems","volume":"23","author":"Jha","year":"2009","journal-title":"Water Resour. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gholizadeh, M.H., Melesse, A.M., and Reddi, L. (2016). A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors, 16.","DOI":"10.3390\/s16081298"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s12145-015-0220-8","article-title":"Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS","volume":"8","author":"Razandi","year":"2015","journal-title":"Earth Sci. Inf."},{"key":"ref_8","unstructured":"Management and Planning Organization (MPO) (2004). Water Resources State Report, Management and Planning Organization (MPO)."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s12665-011-1092-y","article-title":"Assessment of groundwater quality usingmultivariate statistical techniques in Hashtgerd Plain, Iran","volume":"65","author":"Nosrati","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7059","DOI":"10.1007\/s12517-014-1668-4","article-title":"Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS","volume":"8","author":"Rahmati","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s12040-017-0888-x","article-title":"GIS-based bivariate statistical techniques for groundwater potential analysis (an example of Iran)","volume":"126","author":"Haghizadeh","year":"2017","journal-title":"J. Earth Syst. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11269-015-1159-8","article-title":"Remote sensing and GIS based groundwater potential & recharge zonesmapping using multi criteria decision making technique","volume":"30","author":"Agarwal","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1007\/s10661-018-6726-z","article-title":"Monitoring and assessment of seasonal land cover changes using remote sensing: A 30-year (1987\u20132016) case study of Hamoun Wetland, Iran","volume":"190","author":"Kharazmi","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.ecolind.2016.12.017","article-title":"A new indicator of ecosystem water use efficiency based on surface soil moisture retrieved from remote sensing","volume":"75","author":"He","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-015-4376-y","article-title":"Mapping of groundwater potential zones in Salem Chalk Hills, Tamil Nadu, India, using remote sensing and GIS techniques","volume":"187","author":"Thilagavathi","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10040-018-1848-5","article-title":"Groundwater potential mapping using a novel data-mining ensemble model","volume":"27","author":"Kordestani","year":"2018","journal-title":"Hydrogeol. J."},{"key":"ref_17","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_18","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":"1","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s12665-018-7551-y","article-title":"Use of a maximum entropy model to identify the key factors that influence groundwater availability on the Gonabad Plain, Iran","volume":"77","author":"Golkarian","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s41324-017-0127-1","article-title":"Groundwater potential mapping using analytical hierarchical process: A study on Md. Bazar Block of Birbhum District, West Bengal","volume":"25","author":"Saha","year":"2017","journal-title":"Spat. Inf. Res."},{"key":"ref_21","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":"Hydrology"},{"key":"ref_22","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":"Appl. Clim."},{"key":"ref_23","first-page":"1385","article-title":"Erodibility prioritization of subwatersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models","volume":"613","author":"Arabameri","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1007\/s12665-017-7177-5","article-title":"Applying different scenarios for landslide spatial modeling using computational intelligence methods","volume":"76","author":"Arabameri","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Pradhan, B., Pourghasemi, H.R., Rezaei, K., and Kerle, N. (2018). Spatial modeling of gully erosion using GIS and R programing: A comparison among three data mining algorithms. Appl. Sci., 8.","DOI":"10.3390\/app8081369"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1007\/s12665-018-7808-5","article-title":"GIS-based gully erosion susceptibility mapping: A comparison among three data-driven models and AHP knowledge-based technique","volume":"77","author":"Arabameri","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_27","unstructured":"(2018, August 12). Islamic republic of Iran Meteorological Organization (IRIMO). Available online: http:\/\/www. semnanmet.ir."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1002\/hyp.6307","article-title":"Groundwater recharge and discharge in a hyperarid alluvial plain (Akesu, Taklimakan Desert, China)","volume":"21","author":"Tang","year":"2007","journal-title":"Hydrol. Processes"},{"key":"ref_29","unstructured":"(2018, August 12). Geology Survey of Iran (GSI). Available online: http:\/\/www.gsi.ir\/Main\/Lang_en\/index.html."},{"key":"ref_30","unstructured":"Tehran Regional Water Cooperative (TRWC) Company (2000). Simulation Project for Optimum Excavation of Dasht-e-Damghan, Principal Office of Water Resources."},{"key":"ref_31","unstructured":"UNEP (2002). A Survey of Methods for Groundwater Recharge in Arid and Semi-Arid Regions, UNEP\/DEWA\/RS."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.scitotenv.2018.12.115","article-title":"GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches","volume":"658","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_33","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. Model","volume":"2","author":"Jothibasu","year":"2016","journal-title":"Earth Syst. Environ."},{"key":"ref_34","first-page":"16","article-title":"Determination of drainage network in digital elevation model","volume":"2","author":"Kiss","year":"2004","journal-title":"Util. Limit. J. Hung. Geomath."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital terrain modeling: A review of hydrological, geomorphological and biological applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Processes"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A physically based, variable contributing area model of basin hydrology","volume":"24","author":"Beven","year":"1979","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_37","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_38","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_39","unstructured":"Wilson, J.P., and Gallant, J.C. (2000). Primary topographic attributes. Terrain Analysis: Principles and Applications, Wiley."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1016\/j.cageo.2008.12.014","article-title":"Comparison of roving-window and search-windowtechniques for characterising landscape morphometry","volume":"35","author":"Grohmann","year":"2009","journal-title":"Comput. Geosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s00254-007-0818-3","article-title":"GIS based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping","volume":"54","author":"Dahal","year":"2008","journal-title":"Environ. Geol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s11069-011-9879-4","article-title":"Weights of evidence method for landslide susceptibility mapping; Prahova Subcarpathians, Romania","volume":"60","author":"Armas","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11004-013-9511-0","article-title":"Machine learning feature selection methods for landslide susceptibility mapping","volume":"46","author":"Micheletti","year":"2014","journal-title":"Math. Geosci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinf., 9.","DOI":"10.1186\/1471-2105-9-307"},{"key":"ref_46","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, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143865"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Reif, D.M., Motsinger, A.A., McKinney, B.A., Crowe, J.E., and Moore, J.H. (2006, January 28\u201329). Feature Selection using a random forests classifier for the integrated analysis of multiple data type. Proceedings of the 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, Toronto, ON, Canada.","DOI":"10.1109\/CIBCB.2006.330987"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1002\/env.999","article-title":"Incorporating uncertainty in gully erosion calculations using the random forests modelling approach","volume":"21","author":"Kuhnert","year":"2010","journal-title":"Environmetrics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.rse.2014.04.010","article-title":"Random forest classification of salt marsh vegetation habitats using quadpolarimetric airborne SAR, elevation and optical RS data","volume":"149","author":"Comber","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.csda.2007.08.015","article-title":"Empirical characterization of random forest variable importance measures","volume":"52","author":"Archer","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_51","unstructured":"R Development Core Team (2015). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: http:\/\/www.Rproject.org."},{"key":"ref_52","first-page":"2179","article-title":"Point process-based modeling of multiple debris flow landslides using INLA: An application to the 2009 Messina disaster. Stoch","volume":"32","author":"Lombardo","year":"2018","journal-title":"Environ. Res. Risk A"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hwang, C.L., and Yoon, K.P. (1981). Multiple Attribute Decision Making: Methods and Applications, Springer. [1st ed.].","DOI":"10.1007\/978-3-642-48318-9"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1111\/itor.12318","article-title":"Efficiency evaluation of sustainable water management using the HF-TODIM method","volume":"26","author":"Zhang","year":"2019","journal-title":"Int. Trans. Op. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"49","DOI":"10.5267\/j.dsl.2016.8.001","article-title":"TOPSIS with statistical distances: A new approach to MADM","volume":"6","author":"Vomm","year":"2017","journal-title":"Decis. Sci. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Saaty, T.L. (1980). The Analytic Hierarchy Process, McGraw Hill.","DOI":"10.21236\/ADA214804"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Saaty, T.L. (2000). Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, RWS Publications.","DOI":"10.1007\/978-94-015-9799-9_2"},{"key":"ref_58","unstructured":"Lootsma, F.A. (2007). Multi-Criteria Decision Analysis via Ratio and Difference Judgement, Springer. [1st ed.]."},{"key":"ref_59","first-page":"A-06367","article-title":"GIS based landslide susceptibility mapping with comparisons of results from machine learning methods process versus logistic regression in Bailongjiang river basin, China","volume":"10","author":"Bai","year":"2008","journal-title":"Geophys. Res. Abstr. EGU"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.geomorph.2008.02.011","article-title":"Landslide susceptibility mapping based on support vector machine: A case study on natural slopes of Hong Kong, China","volume":"101","author":"Yao","year":"2008","journal-title":"Geomorphology"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). Nature of Statistical Learning Theory, Wiley.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_62","first-page":"155","article-title":"Uniform object generation for optimizing one class classifiers","volume":"2","author":"Tax","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining Inference and Prediction, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.envsoft.2017.08.003","article-title":"Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO penalized Generalized Linear Model","volume":"97","author":"Camilo","year":"2018","journal-title":"Environ. Model. Softw."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yesilnacar, E.K. (2005). The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. [Ph.D. Thesis, Department of Geomatics the University of Melbourne].","DOI":"10.1007\/1-4020-2409-6_1"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s00254-003-0917-8","article-title":"A comparison of the GIS based landslide susceptibility assessment methods: Multivariate versus bivariate","volume":"45","author":"Doyuran","year":"2004","journal-title":"Environ. Geol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Dao, D.V., Trinh, S.H., Ly, H.-B., and Pham, B.T. (2019). Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. Appl. Sci., 9.","DOI":"10.3390\/app9061113"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Dao, D.V., Ly, H.-B., Trinh, S.H., Le, T.-T., and Pham, B.T. (2019). Rtificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete. Materials, 12.","DOI":"10.3390\/ma12060983"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ly, H.-B., Monteiro, E., Le, T.-T., Le, V.M., Dal, M., and Regnier, G. (2019). Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees. Materials, 12.","DOI":"10.3390\/ma12091544"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.catena.2018.10.004","article-title":"A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil","volume":"173","author":"Pham","year":"2019","journal-title":"Catena"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s12594-018-0862-5","article-title":"A novel classifier based on composite hyper-cubes on iterated random projections for assessment of landslide susceptibility","volume":"91","author":"Pham","year":"2018","journal-title":"J. Geol. Soc. India"},{"key":"ref_73","unstructured":"Saltelli, A., Chan, K., and Scott, E.M. (2000). Sensitivity Analysis, Wiley."},{"key":"ref_74","first-page":"1543","article-title":"Uncertainty in the environmental modelling process\u2014A framework and guidance","volume":"22","author":"Refsgaard","year":"2007","journal-title":"Water Resour. Manag."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/13658810110053125","article-title":"Uncertainty and sensitivity analysis: Tools for GIS-based model implementation","volume":"15","author":"Crosetto","year":"2001","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.scitotenv.2016.02.133","article-title":"Trends in sensitivity analysis practice in the last decade","volume":"568","author":"Ferretti","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1016\/j.envsoft.2010.06.001","article-title":"Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation","volume":"25","author":"Chen","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1080\/02693799008941556","article-title":"Attribute error and sensitivity analysis of map operations in geographical information systems: Suitability analysis","volume":"4","author":"Lodwick","year":"1990","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jhydrol.2010.12.027","article-title":"GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea","volume":"399","author":"Oh","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10040-014-1198-x","article-title":"Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia","volume":"23","author":"Fenta","year":"2015","journal-title":"Hydrogeol. J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-015-2166-z","article-title":"Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing","volume":"9","author":"Tahmassebipoor","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.envsoft.2013.10.001","article-title":"Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MAXENT","volume":"51","author":"Convertino","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s12665-014-3442-z","article-title":"Using maximum entropymodeling for landslide susceptibility mapping with multiple geoenvironmental data sets","volume":"73","author":"Park","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_84","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 Vietnamusing statistical index and logistic regression","volume":"59","author":"Lofman","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.geomorph.2017.03.025","article-title":"Improving transferability strategies for debris flow susceptibility assessment. Application to the Saponara and Itala catchments (Messina, Italy)","volume":"288","author":"Cama","year":"2017","journal-title":"Geomorphology"},{"key":"ref_86","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_87","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40677-019-0126-8","article-title":"Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India","volume":"6","author":"Roy","year":"2019","journal-title":"Geoenvironmental Disasters"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geomorph.2009.10.002","article-title":"Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA","volume":"115","author":"Regmi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s12517-013-1161-5","article-title":"Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iraq","volume":"8","author":"Moghaddam","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"189","DOI":"10.3189\/172756407782871792","article-title":"DEM quality assessment for quantification of glacier surface change","volume":"46","author":"Pope","year":"2014","journal-title":"Ann. Glaciol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"9475","DOI":"10.3390\/rs6109475","article-title":"Evaluating the quality and accuracy of TanDEM-X digital elevation models at archaeological sites in the Cilician Plain, Turkey","volume":"6","author":"Erasmi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Alganci, U., Besol, B., and Sertel, E. (2018). Accuracy assessment of different digital surface models. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030114"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.jseaes.2012.10.005","article-title":"Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster\u2013Shafer, and weights-of-evidence models","volume":"61","author":"Mohammady","year":"2012","journal-title":"J. Asian Earth Sci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jhydrol.2013.09.034","article-title":"Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS","volume":"504","author":"Tehrany","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_96","unstructured":"Hong, H., Tsangaratos, P., Ilia, L., Chen, W., and Xu, C. (June, January 29). Comparing the performance of a logistic regression and a random forest model in landslide susceptibility assessments. The Case of Wuyaun Area, China. Proceedings of the Workshop World Landslide Forum, Ljubljana, Slovenia."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1016\/j.proeng.2018.01.135","article-title":"Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka)","volume":"212","author":"Hemasinghe","year":"2018","journal-title":"Procedia Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/3015\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:42:22Z","timestamp":1760190142000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/24\/3015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,14]]},"references-count":97,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11243015"],"URL":"https:\/\/doi.org\/10.3390\/rs11243015","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,14]]}}}