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Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R2 = 0.999, RMSE = 29.90) and testing datasets (R2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human

Groundwater salinization is one of the most severe environmental problems in coastal aquifers worldwide, causing exceeding salinity in groundwater supply systems for many purposes. High salinity concentration in groundwater can be detected several kilometers inland and may result in an increased risk for coastal water supply systems and human health problems. This study investigates the impacts of groundwater pumping practices and regional groundwater flow dynamics on groundwater flow and salinity intrusion in the coastal aquifers of the Vietnamese Mekong Delta using the SEAWAT model—a variable-density groundwater flow and solute transport model. The model was constructed in three dimensions (3D) and accounted for multi-aquifers, variation of groundwater levels in neighboring areas, pumping, and paleo-salinity. Model calibration was carried for 13 years (2000 to 2012), and validation was conducted for 4 years (2013 to 2016). The best-calibrated model was used to develop prediction models for the next 14 years (2017 to 2030). Six future scenarios were introduced based on pumping rates and regional groundwater levels. Modeling results revealed that groundwater pumping activities and variation of regional groundwater flow systems strongly influence groundwater level depletion and saline movement from upper layers to lower layers. High salinity (>2.0 g/L) was expected to expand downward up to 150 m in depth and 2000 m toward surrounding areas in the next 14 years under increasing groundwater pumping capacity. A slight recovery in water level was also observed with decreasing groundwater exploitation. The reduction in the pumping rate from both local and regional scales will be necessary to recover groundwater levels and protect fresh aquifers from expanding paleo-saline in groundwater.

Groundwater is a primary freshwater source for various domestic, industrial and agricultural purposes, especially in coastal regions where there are lacking surface water supply. However, groundwater quality in coastal regions is often threatened by seawater intrusion and contamination due to both anthropogenic activities and natural processes. Therefore, insights into groundwater geochemistry and occurrences are necessary for sustainable groundwater management in coastal regions. The main aim of this study is to investigate the hydrogeochemical characteristics and their influencing factors in a coastal area of the Mekong Delta, Vietnam (MD). A total of 286 groundwater samples were taken from shallow and deep aquifers for analyzing major ions and stable isotopes. The results show that deep groundwater is dominated by Ca–HCO3, Ca–Na–HCO3, Ca–Mg–Cl, and Na–HCO3 while shallow groundwater is dominated by the Na–Cl water type. In this region, the main geochemical processes controlling groundwater chemistry are ion exchanges, mineralization and evaporation. Groundwater salinization in coastal aquifers of the Mekong Delta is caused by (1) paleo-seawater intrusion and evaporation occurring in the Holocene and Pleistocene aquifers, (2) dissolution of salt sediment/rock and leakage of saline from upper to lower aquifers due to excessive groundwater exploitation and hydraulic connection. High nitrate concentrations in both shallow and deep aquifers are related to human activities. These results imply that groundwater extraction may exacerbate groundwater quality-related problems and suitable solutions for sustainable groundwater management in the coastal area of the Mekong Delta are needed.

QC 20140903