Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity in multi-layered aquifers spanning the entire Mekong Delta (MD) region using machine learning (ML) models based on an in situ dataset and using three indicators (Cl, pH, and HCO). We applied nine different decision tree-based models and evaluated their prediction performances.

A model for the evolution in water chemistry of an arsenic contaminated aquifer over the last 6000 years, Red River floodplain, Vietnam.

Aquifers on the Red River flood plain with burial ages ranging from 500 to 6000 years show, with increasing age, the following changes in solute concentrations; a decrease in arsenic, increase in Fe(II) and decreases in both pH, Ca and bicarbonate. These changes were interpreted in terms of a reaction network comprising the kinetics of organic carbon degradation, the reduction kinetics of As containing Fe-oxides, the sorption of arsenic, the kinetics of siderite precipitation and dissolution, as well as of the dissolution of CaCO. The arsenic released from the Fe-oxide is preferentially partitioned into the water phase, and partially sorbed, while the released Fe(II) is precipitated as siderite.