Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam

• Published in: Ecological indicators Vol. 127; p. 107790
• Main Authors: Tran, Dang An, Tsujimura, Maki, Ha, Nam Thang, Nguyen, Van Tam, Binh, Doan Van, Dang, Thanh Duc, Doan, Quang-Van, Bui, Dieu Tien, Anh Ngoc, Trieu, Phu, Le Vo, Thuc, Pham Thi Bich, Pham, Tien Dat
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2021; Elsevier
• Subjects: algorithms; Bayesian theory; CatBoost Regression; data collection; groundwater; Groundwater salinization; human health; hydraulic conductivity; Influencing factors; Mekong Delta; Mekong River; model validation; Multi-layer coastal aquifers; prediction; remediation; river deltas; spatial data; Vietnam; water management; water salinity; water salinization; water table; Vietnam; Mekong River; Multi-layer coastal aquifers; Groundwater salinization; Influencing factors; CatBoost Regression; Mekong Delta

[Display omitted] •Top ten influencing factors of groundwater salinization were identified.•The CatBoost Regression model provides the highest accuracy salinity prediction.•In the Mekong Delta, groundwater pumping has a strong impact on salinization processes.•Forty-eight percentage of the populatio...