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Fresh-saline groundwater is distributed in a highly heterogeneous way throughout the world. Groundwater salinization is a serious environmental issue that harms ecosystems and public health in coastal regions worldwide. Because of the complexities of groundwater salinization processes and the variables that influence them, it is challenging to predict groundwater salinity concentrations precisely. It compares cutting-edge machine learning (ML) algorithms for predicting groundwater salinity and identifying contributing factors. It employs bi-directional long short-term memory (BiLSTM) to indicate groundwater salinity. The input variable selection problem has attracted attention in the time series modeling community because it has been shown that information-theoretic input variable selection algorithms provide a more accurate representation of the modeled process than linear alternatives. To generate sample combinations for training multiple BiLSTM models, PMIS-selected predictors are used, and the predicted values from various BiLSTM models are also used to calculate the degree of prediction uncertainty for groundwater levels. The findings give policymakers insights for recommending groundwater salinity remediation and management strategies in the context of excessive groundwater exploitation in coastal lowland regions. To ensure sustainable groundwater management in coastal areas, it is essential to recognize the significant impact of human-caused factors on groundwater salinization.

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淡水咸地下水在世界各地以高度异质的方式分布。地下水盐化是一个严重的环境问题，危害全球沿海地区的生态系统和公众健康。由于地下水盐化过程及其影响变量的复杂性，精确预测地下水盐度浓度具有挑战性。它比较了用于预测地下水盐度和识别影响因素的尖端机器学习 (ML) 算法。它采用双向长短期记忆（BiLSTM）来指示地下水盐度。输入变量选择问题引起了时间序列建模界的关注，因为事实证明，信息论输入变量选择算法比线性算法提供了更准确的建模过程表示。为了生成用于训练多个 BiLSTM 模型的样本组合，使用了 PMIS 选择的预测器，并且还使用各种 BiLSTM 模型的预测值来计算地下水位的预测不确定性程度。研究结果为政策制定者提供了在沿海低地地区地下水过度开采的背景下建议地下水盐度修复和管理策略的见解。为了确保沿海地区地下水的可持续管理，必须认识到人为因素对地下水盐化的重大影响。