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 HCO3−). We applied nine different decision tree-based models and evaluated their prediction performances. The models were trained using 13 input variables: weather (2), hydrogeological conditions (4), water levels (3), groundwater usage (2), and relative distance from water sources (2). Subsequently, by employing model interpretation techniques, we quantified the significance of factors within the model prediction. Performance evaluations of the ML models demonstrated that the Extra Trees model exhibited superior performance and demonstrated generalization capabilities in predicting Cl− concentration, whereas the Bagging and Random Forest models outperformed the other models in predicting pH and HCO3− concentration. The coefficients of determination were determined to be 0.94, 0.67, and 0.78 for Cl−, pH, and HCO3−, respectively Additionally, the model interpretation effectively identified significant factors that depended on the target variables and aquifers. In particular, salinity indicators and aquifers that were strongly influenced by the artificial usage of groundwater were identified. Therefore, our research, which provides accurate spatial predictions and interpretations of groundwater salinity in the MD, has the potential to establish a foundation for formulating effective groundwater management policies to control groundwater salinization.

中文翻译：

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使用可解释的机器学习模型对湄公河三角洲地区多个含水层地下水盐度进行空间预测


地下水盐碱化是沿海地区普遍存在的问题，但由于地下水系统的复杂性，准确预测和了解其成因仍然具有挑战性。因此，本研究使用基于原位数据集的机器学习 (ML) 模型并使用三个指标（Cl−、pH 和 HCO3−）预测了跨越整个湄公河三角洲 (MD) 地区的多层含水层的地下水盐度。我们应用了九种不同的基于决策树的模型并评估了它们的预测性能。使用 13 个输入变量对模型进行训练：天气 (2)、水文地质条件 (4)、水位 (3)、地下水使用量 (2) 以及距水源的相对距离 (2)。随后，通过采用模型解释技术，我们量化了模型预测中因素的重要性。 ML 模型的性能评估表明，Extra Trees 模型在预测 Cl− 浓度方面表现出优越的性能和泛化能力，而 Bagging 和随机森林模型在预测 pH 和 HCO3− 浓度方面优于其他模型。 Cl−、pH 和 HCO3− 的确定系数分别确定为 0.94、0.67 和 0.78。此外，模型解释有效地识别了取决于目标变量和含水层的重要因素。特别是，确定了受人工使用地下水强烈影响的盐度指标和含水层。因此，我们的研究为马里兰州地下水盐度提供了准确的空间预测和解释，有可能为制定有效的地下水管理政策以控制地下水盐化奠定基础。