Spatial prediction of groundwater potentiality using machine learning methods with Grey Wolf and Sparrow Search Algorithms

• Published in: Journal of hydrology (Amsterdam) 2022-07, Vol.610, p.127977, Article 127977
• Main Authors: Liu, Rui, Li, Gulin, Wei, Liangshuai, Xu, Yuan, Gou, Xiaojuan, Luo, Shubin, Yang, Xin
• Format: Article
• Language: English
• Subjects: Groundwater potential maps; GWO; Hybrid models; Machine learning; SSA
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2022.127977

•Four hybrid models were constructed for groundwater potential mapping and different sample datasets (D1–D3) were used to increase the confidence of the result.•The Sparrow Search Algorithm was first used in groundwater modeling.•The correlation between conditioning factors and groundwater occurrence were analyze.•The hybrid models were validated using ROC curve and related statistical indexes.•All hybrid models obtained high accuracy on the training and validation datasets, and the Sparrow Search Algorithm performs better. Groundwater is considered to be one of the most valuable natural resources in the world. However, the availability of groundwater is of concern. Therefore, understanding the potential of groundwater is very important for the utilization of water resources. The main goal of the study was to predict and assess the groundwater using hybrid machine learning and metaheuristic algorithms to automatically tune the parameters, namely the Random Forest (RF), Support Vector Machines (SVM), the Grey Wolf Algorithm (GWO), and the Sparrow Search Algorithm (SSA). A total of 608 groundwater locations were identified by field surveys. Three different sample datasets (D1-D3) were created to increase the confidence of the result, and each dataset was divided randomly into a training set (70%) and a validation set (30%). Fifteen conditioning factors involving geology, human activity, and hydrology were extracted from the available materials. The Evidential Belief Function (EBF) was employed to determine the correlation between groundwater and factors. Then fourteen relevant factors were selected by feature selection. After that, the hybrid models of RF-GWO, RF-SSA, SVM-GWO, and SVM-SSA built using the datasets (D1-D3) were applied to generate groundwater potential maps (GPMs). Results showed that the performances of the hybrid models can be considered to be stable. The global performance of these hybrid models was assessed using the area under the receiver operating characteristic curve (AUC-ROC) and related statistical indexes. According to the D1 dataset validation results, the AUC values for the RF-GWO, RF-SSA, SVM-GWO, and SVM-SSA were 0.832, 0.840, 0.790, and 0.809, respectively. The RF-SSA had the highest accuracy (0.764), with an AUC of 0.840, and the SVM-GWO showed the least accuracy (0.723), with an AUC of 0.790. The outcomes revealed that all hybrid models had a good predictive performance. However, the RF outperformed the SVM model, and the SSA