Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models

作     者：Barzegar, Rahim Razzagh, Siamak Quilty, John Adamowski, Jan Pour, Homa Kheyrollah Booij, Martijn J. 

作者机构：McGill Univ Dept Bioresource Engn 21111 Lakeshore Ste Anne De Bellevue PQ H9X 3V9 Canada Wilfrid Laurier Univ Dept Geog & Environm Studies Waterloo ON Canada Univ Tabriz Fac Nat Sci Dept Earth Sci Tabriz Iran Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada Univ Twente Fac Engn Technol Dept Water Engn & Management Enschede Netherlands 

出 版 物：《JOURNAL OF HYDROLOGY》 (水文学杂志)

年 卷 期：2021年第598卷

页      面：126370-126370页

核心收录：

学科分类：08[工学] 0708[理学-地球物理学] 081501[工学-水文学及水资源] 0815[工学-水利工程] 0814[工学-土木工程] 

基　　金：The authors would like to acknowledge the East Azarbaijan Regional Water Authority for supplying data. We would also like to thank Dr. Asghar Asghari Moghaddam for helping with water sampling and analysis. We thank all reviewers and the editors for their very helpful comments that improved the clarity and quality of the final manuscript 

主　　题：Resampling algorithm Groundwater vulnerability Coastal aquifer Machine learning Hybrid model 

摘      要：Developing accurate groundwater vulnerability maps is important for the sustainable management of groundwater resources. In this research, resampling methods [e.g., Bootstrap Aggregating (BA) and Disjoint Aggregating (DA)] are combined with machine learning (ML) models, namely eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Random Forest (RF), to improve the GALDIT groundwater vulnerability mapping framework that considers Groundwater occurrence (G) (i.e., aquifer type), Aquifer hydraulic conductivity (A), depth to groundwater Level (L), Distance from the seashore (D), Impact of existing seawater intrusion status (I), and aquifer Thickness (T). The proposed approach overcomes the subjectivity of the weights and ratings given to the six variables in the GALDIT framework (via the ML methods) and helps address the small dataset issue (via resampling methods) common to groundwater vulnerability predictive mapping. Considering the Shabestar Plain aquifer, situated in the northeast of Lake Urmia (Iran), the predicted vulnerability indices from GALDIT were adjusted using total dissolved solid (TDS, an indicator of drinking water quality) concentrations, and were modeled by the ML models. Pearson s correlation coefficient (r) and distance correlation (DC) between the predicted vulnerability indices and TDS were used to validate the models. Using a validation set, the GALDIT framework (r = 0.447 and DC = 0.511) was compared against the best performing standalone (XGBoost-GALDIT, r = 0.613, DC = 0.647) and coupled resampling (BA-XGBoost-GALDIT, r = 0.659, DC = 0.699 and DA-RF-GALDIT, r = 0.616, DC = 0.662) ML models, revealing that the proposed framework significantly increases r and DC metrics. In general, the BA resampling method led to better performing ML models than DA. However, in all cases, it was found that integrating resampling methods and ML models are promising tools to