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.
Highlights
As pointed out by many researchers, one of the most important natural resource worldwide is groundwater, with one third of the world’s population depending on it [1,2,3,4]
The current study presents a novel hybrid integration approach of Frequency Ratio (FR) with artificial intelligence-based kernel logistic regression (KLR), alternating decision tree (ADTree), and random forest (RF) models for groundwater spring potential mapping, having as a test site the Ningtiaota region, China
The developed investigation approach followed in the present study was a four-step procedure: Theselection, developed investigation approach in map the present study was four-step procedure: (i) data generation of the spring followed inventory and selection of anonspring areas, (ii) data selection, generation of the spring inventory map and selection of nonspring areas, (ii) application application of the Frequency Ration (FR) method and the linear support vector machine (LSVM) as a of the Frequency method and linear support vector machinefactor (LSVM)
Summary
As pointed out by many researchers, one of the most important natural resource worldwide is groundwater, with one third of the world’s population depending on it [1,2,3,4]. Several areas in the world are subject to overexploitation of groundwater, undergoing water shortages as a result of a difference between water supply and demand [5]. Sci. 2020, 10, 425 will increase substantially in the following years, mainly due to the growing population and economic development [6,7,8,9,10]. According to Curran and de Sherbinin [11], even though the supply of water is mainly controlled by climatic parameters, the management and the followed practices significantly influence the availability of water. In the case of groundwater resources, inappropriate management may result in the deterioration of water resources and a decrease in the quality of groundwater [12]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call