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

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dc.contributor.authorOzdemir, Adnan
dc.date.accessioned2020-03-26T18:14:47Z
dc.date.available2020-03-26T18:14:47Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, groundwater spring potential maps produced by three different methods, frequency ratio, weights of evidence, and logistic regression, were evaluated using validation data sets and compared to each other. Groundwater spring occurrence potential maps in the Sultan Mountains (Konya, Turkey) were constructed using the relationship between groundwater spring locations and their causative factors. Groundwater spring locations were identified in the study area from a topographic map. Different thematic maps of the study area, such as geology, topography, geomorphology, hydrology, and land use/cover, have been used to identify groundwater potential zones. Seventeen spring-related parameter layers of the entire study area were used to generate groundwater spring potential maps. These are geology (lithology), fault density, distance to fault, relative permeability of lithologies, elevation, slope aspect, slope steepness, curvature, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport capacity index, drainage density, distance to drainage, land use/cover, and precipitation. The predictive capability of each model was determined by the area under the relative operating characteristic curve. The areas under the curve for frequency ratio, weights of evidence and logistic regression methods were calculated as 0.903, 0.880, and 0.840, respectively. These results indicate that frequency ratio and weights of evidence models are relatively good estimators, whereas logistic regression is a relatively poor estimator of groundwater spring potential mapping in the study area. The frequency ratio model is simple; the process of input, calculation and output can be readily understood. The produced groundwater spring potential maps can serve planners and engineers in groundwater development plans and land-use planning. (C) 2011 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.jhydrol.2011.10.010en_US
dc.identifier.endpage308en_US
dc.identifier.issn0022-1694en_US
dc.identifier.issn1879-2707en_US
dc.identifier.issue03.04.2020en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage290en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.jhydrol.2011.10.010
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26524
dc.identifier.volume411en_US
dc.identifier.wosWOS:000298200200011en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofJOURNAL OF HYDROLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectFrequency ratioen_US
dc.subjectWeights of evidenceen_US
dc.subjectLogistic regressionen_US
dc.subjectGroundwater potentialen_US
dc.subjectSpringen_US
dc.subjectGISen_US
dc.titleGIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparisonen_US
dc.typeArticleen_US

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