Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

• Published in: Journal of hydrology (Amsterdam) Vol. 405; no. 1; pp. 123 - 136
• Main Author: Ozdemir, Adnan
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 21.07.2011; Elsevier
• Subjects: Areal geology; Areal geology. Maps; Curvature; Density; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geologic maps, cartography; GIS; Groundwater; Groundwater potential; Hydrogeology; Hydrology. Hydrogeology; Logistic regression; Logistics; Mathematical models; Mountains; Regression; Spring; Springs; The Sultan Mountains; Water resources; Middle East; Asia; Turkey; GIS; Logistic regression; The Sultan Mountains; Spring; Groundwater potential; mountains; springs; lithology; permeability; regression analysis; ground water; accuracy; geographic information systems; faults; land use; aquifers; land cover; models; exploration; maps; atmospheric precipitation; streams; digital elevation models; sediment transport; interpretation; water resource management; drainage
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2011.05.015

► The logistic regression approach has not yet been used to delineate groundwater potential zones. ► In this study we attempted to identify groundwater potential zones using logistic regression method. ► The logistic regression method was used to locate potential zones for groundwater in the Sultan Mountains. ► The evolved model was found to be in strong agreement with available groundwater spring test data. ► Hence, this method can be used routinely in groundwater exploration in favourable geological conditions. The purpose of this study is to produce a groundwater spring potential map of the Sultan Mountains in central Turkey, based on a logistic regression method within a Geographic Information System (GIS) environment. Using field surveys, the locations of the springs (440 springs) were determined in the study area. In this study, 17 spring-related factors were used in the analysis: geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transport capacity index, distance to drainage, distance to fault, drainage density, and fault density map. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the groundwater spring potential for the entire study area. The accuracy of the final spring potential map was evaluated based on the observed springs. The accuracy of the model was evaluated by calculating the relative operating characteristics. The area value of the relative operating characteristic curve model was found to be 0.82. These results indicate that the model is a good estimator of the spring potential in the study area. The spring potential map shows that the areas of very low, low, moderate and high groundwater spring potential classes are 105.586 km 2 (28.99%), 74.271 km 2 (19.906%), 101.203 km 2 (27.14%), and 90.05 km 2 (24.671%), respectively. The interpretations of the potential map showed that stream power index, relative permeability of lithologies, geology, elevation, aspect, wetness index, plan curvature, and drainage density play major roles in spring occurrence and distribution in the Sultan Mountains. The logistic regression approach has not yet been used to delineate groundwater potential zones. In this study, the logistic regression method was used to locate potential zones for groundwater springs in the Sultan Mountains. The evolved model was found to be in strong agreement with the available groundwater spring test data. Hence, this method can be used routinely in groundwater exploration under favourable conditions.