Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

Abstract

This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundwater springs and fourteen explanatory factors (elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use), which were divided into training and validation datasets. Ningtiaota region in the northern territory of Shaanxi Province, China, was considered as a test site. Frequency Ratio method was applied to provide to each factor’s class a coefficient weight, whereas the linear support vector machine method was used as a feature selection method to determine the optimal set of factors. The Receiver Operating Characteristic curve and the area under the curve (AUC) were used to evaluate the performance of each model using the training dataset, with the RF model providing the highest AUC value (0.909) followed by the KLR (0.877) and ADTree (0.812) models. The same performance pattern was estimated based on the validation dataset, with the RF model providing the highest AUC value (0.811) followed by the KLR (0.797) and ADTree (0.773) models. This study highlights that the artificial intelligence approach could be considered as a valid and accurate approach for groundwater spring potential zoning.

Keywords

• groundwater spring potential mapping
• kernel logistic regression
• random forest
• alternating decision tree
• china