This paper presents an integrated framework for management of aquifers threatened by saltwater intrusion (SI). In this framework, SEAWAT model is used for simulating the density dependent groundwater flow. Three meta-models based on the artificial neural network (ANN), M5 tree and random subspaces model (RSM) are developed, as surrogate models for SEAWAT to accurately simulate the groundwater response to different pumping and recharge scenarios. Various patterns of recharge to and discharge from aquifer are used to generate a database for training the mentioned surrogate models. To decrease the number of training parameters, the aquifer area is divided into different zones using k-means clustering technique (KMC). Additionally, a conjunctive model (CM) using a combination of the three surrogate models is proposed to enhance the accuracy of the simulation. It is then integrated with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with the objectives of maximizing pumping rates and minimizing SI length. Next, the socially optimal scenarios are selected from the obtained Pareto-front using the Nash bargaining theory. The performance of the proposed model is evaluated by applying it to the Kahak aquifer, Iran, which is subjected to SI. The results show that the conjunctive model using KMC technique predicts SI length with a comparable accuracy and results in 95% reduction in runtime compared to a simulation-optimization (SO) model.

本文提出了一个综合框架，用于管理受海水入侵（SI）威胁的含水层。在此框架中，SEAWAT模型用于模拟与密度有关的地下水流。作为SEAWAT的替代模型，开发了基于人工神经网络（ANN），M5树和随机子空间模型（RSM）的三个元模型，以准确模拟地下水对不同抽水和补给情景的响应。各种含水层补给和排放方式被用来生成一个数据库，用于训练上述替代模型。为了减少训练参数的数量，使用k均值聚类技术（KMC）将含水层区域划分为不同的区域。另外，提出了结合使用三个替代模型的联合模型（CM），以提高模拟的准确性。然后将其与非支配排序遗传算法II（NSGA-II）集成在一起，目的是最大化抽水速率并最小化SI长度。接下来，使用纳什讨价还价理论从获得的帕累托锋中选择社会最优方案。拟议模型的性能通过将其应用于受到SI限制的伊朗Kahak含水层进行评估。结果表明，与模拟优化（SO）模型相比，使用KMC技术的联合模型可预测SI长度并具有可比的准确性，并且可将运行时间减少95％。然后将其与非支配排序遗传算法II（NSGA-II）集成在一起，目的是最大化抽水速率并最小化SI长度。接下来，使用纳什讨价还价理论从获得的帕累托锋中选择社会最优方案。拟议模型的性能通过将其应用于受到SI限制的伊朗Kahak含水层进行评估。结果表明，与模拟优化（SO）模型相比，使用KMC技术的联合模型可预测SI长度并具有可比的准确性，并且可将运行时间减少95％。然后将其与非支配排序遗传算法II（NSGA-II）集成在一起，以最大程度地提高抽水速率并最小化SI长度。接下来，使用纳什讨价还价理论从获得的帕累托锋中选择社会最优方案。拟议模型的性能通过将其应用于受到SI限制的伊朗Kahak含水层进行评估。结果表明，与模拟优化（SO）模型相比，使用KMC技术的联合模型可预测SI长度并具有可比的准确性，并且可将运行时间减少95％。