Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

Soyoung Park, Se-Yeong Hamm, Hang-Tak Jeon and Jinsoo Kim
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Soyoung Park: BK21 Plus Project of the Graduate School of Earth Environmental Hazard System, Pukyong National University, Busan 48513, Korea
Se-Yeong Hamm: Department of Geological Sciences, Pusan National University, Busan 46241, Korea
Hang-Tak Jeon: Department of Geological Sciences, Pusan National University, Busan 46241, Korea
Jinsoo Kim: Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea

Sustainability, 2017, vol. 9, issue 7, 1-20

Abstract: This study mapped and analyzed groundwater potential using two different models, logistic regression (LR) and multivariate adaptive regression splines (MARS), and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ?70 m 3 /d were extracted, and 859 locations (70%) were used for model training, whereas the other 365 locations (30%) were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs) were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC) for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

Keywords: groundwater potential; logistic regression; multivariate adaptive regression splines; groundwater potential map; groundwater potential analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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