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

Soyoung Park,Se-Yeong Hamm,Jinsoo Kim,Hang-Tak Jeon

doi:10.3390/su9071157

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 m3/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.

Highlights

Groundwater is defined as water in the saturated zone that fills the pore spaces between mineral grains and the cracks and fractures within a rock mass [1]
Multicollinearity and frequency ratio (FR) were analyzed as preliminary analyses prior to groundwater potential assessment
Multicollinearity can be assessed by various means, and tolerance (TOL) and variance inflation factor (VIF) assessments were used in this study

Summary

Introduction

Groundwater is defined as water in the saturated zone that fills the pore spaces between mineral grains and the cracks and fractures within a rock mass [1]. It results from the interactions of climatic, geological, hydrological, physiographical, and ecological factors [2]. Groundwater makes up 50% of the present potable water supplies, 40% of the industrial water demand, and 20% of the water used for irrigation [3]. It is an essential element of life, and an essential natural resource. Because groundwater is a limited resource, it is necessary to devise effective and efficient plans to use it based on an understanding of the behavior of groundwater systems and identification of the current status of the local groundwater system through groundwater exploration [5]

Objectives

Methods

Results