For more than one billion people living in coastal regions, coastal aquifers provide a water resource. In coastal regions, monitoring water quality is an important issue for policymakers. Many studies mentioned that most of the conventional models were not accurate for predicting total dissolved solids (TDS) and electrical conductivity (EC) in coastal aquifers. Therefore, it is crucial to develop an accurate model for forecasting TDS and EC as two main parameters for water quality. Hence, in this study, a new hybrid deep learning model is presented based on Convolutional Neural Networks (CNNE), Long Short-Term Memory Neural Networks (LOST), and Gaussian Process Regression (GPRE) models. The objective of this study will contribute to the sustainable development goal (SDG) 6 of the united nation program which aims to guarantee universal access to clean water and proper sanitation. The new model can obtain point and interval predictions simultaneously. Additionally, features of data points can be extracted automatically. In the first step, the CNNE model automatically extracted features. Afterward, the outputs of CNNE were flattened. The LOST used flattened arrays for the point prediction. Finally, the outputs of the GPRE model receives the outputs of the LOST model to obtain the interval prediction. The model parameters were adjusted using the rat swarm optimization algorithm (ROSA). This study used PH, Ca + + , Mg2 + , Na + , K + , HCO₃, SO4, and Cl⁻ to predict EC and TDS in a coastal aquifer. For predicting EC, the CNNE-LOST-GPRE, LOST-GPRE, CNNE-GPRE, CNNE-LOST, LOST, and CNNE models achieved NSE values of 0.96, 0.95, 0.92, 0.91, 0.90, and 0.87, respectively. Sodium adsorption ratio, EC, magnesium hazard ratio, sodium percentage, and total hardness indices were used to evaluate the quality of GWL. These indices indicated poor groundwater quality in the aquifer. This study shows that the CNNE-LOST-GPRE is a reliable model for predicting complex phenomena. Therefore, the current developed hybrid model could be used by private and public water sectors for predicting TDS and EC for enhancing water quality in coastal aquifers.

沿海含水层为生活在沿海地区的超过十亿人提供了水资源。在沿海地区，水质监测对于决策者来说是一个重要问题。许多研究提到，大多数传统模型对于预测沿海含水层中的总溶解固体（TDS）和电导率（EC）并不准确。因此，开发准确的模型来预测 TDS 和 EC 作为水质的两个主要参数至关重要。因此，在本研究中，提出了一种基于卷积神经网络（CNNE）、长短期记忆神经网络（LOST）和高斯过程回归（GPRE）模型的新混合深度学习模型。这项研究的目标将为联合国计划的可持续发展目标 (SDG) 6 做出贡献，该目标旨在保证普遍获得清洁水和适当的卫生设施。新模型可以同时获得点预测和区间预测。此外，可以自动提取数据点的特征。第一步，CNNE 模型自动提取特征。此后，CNNE的产出就持平了。LOST 使用扁平化数组进行点预测。最后，GPRE模型的输出接收LOST模型的输出，得到区间预测。使用大鼠群优化算法（ROSA）调整模型参数。本研究使用 PH、Ca++、Mg2+、Na+、K+、HCO3 _、 SO4 和 Cl-^来预测沿海含水层的 EC 和 TDS。为了预测 EC，CNNE-LOST-GPRE、LOST-GPRE、CNNE-GPRE、CNNE-LOST、LOST 和 CNNE 模型的 NSE 值分别为 0.96、0.95、0.92、0.91、0.90 和 0.87。采用钠吸附比、EC、镁风险比、钠百分率、总硬度等指标评价GWL的质量。这些指标表明含水层地下水质量较差。这项研究表明 CNNE-LOST-GPRE 是预测复杂现象的可靠模型。因此，当前开发的混合模型可供私营和公共水部门用于预测 TDS 和 EC，以提高沿海含水层的水质。