ABSTRACT Linked simulation-optimisation (S–O) models need to simulate the physical processes either by using a rigorous numerical model, or a trained surrogate model approximating the physical processes. A methodology is proposed to evolve Pareto optimal management strategies for a multi-layered coastal aquifer system using a trained and tested Multivariate Adaptive Regression Spline (MARS) surrogate model linked to a multi-objective saltwater intrusion management model. Performance of the developed methodology is evaluated using an illustrative multi-layered coastal aquifer system. Solution results indicate that MARS is capable of approximately replacing the more rigorous numerical simulation model within the linked S–O model to ensure computational efficiency and feasibility in applying such linked S–O models for coastal aquifer management problems. Furthermore, the ability of MARS to recognise the most relevant input variables in predicting the responses as outputs enables the construction of an efficient and robust surrogate model. Integration of parallel processing capabilities within the optimisation model is shown to improve computational efficiency and feasibly of solving such large scale multi-objective problems. Therefore, the developed methodology utilising the MARS based surrogate model is potentially applicable for developing optimal groundwater extraction strategies for sustainable regional scale management of coastal aquifers.

中文翻译：

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基于代理的多目标管理模型控制多层沿海含水层系统中的盐水入侵

摘要链接模拟优化 (S-O) 模型需要通过使用严格的数值模型或经过训练的近似物理过程的替代模型来模拟物理过程。提出了一种方法，使用与多目标咸水入侵管理模型相关联的经过训练和测试的多元自适应回归样条 (MARS) 替代模型，为多层沿海含水层系统制定帕累托最优管理策略。所开发方法的性能使用说明性的多层沿海含水层系统进行评估。求解结果表明，MARS 能够近似替代链接 S-O 模型中更严格的数值模拟模型，以确保将此类链接 S-O 模型应用于沿海含水层管理问题的计算效率和可行性。此外，MARS 在将响应预测为输出时识别最相关的输入变量的能力使构建高效且稳健的替代模型成为可能。优化模型中并行处理能力的集成被证明可以提高计算效率，并且可以解决此类大规模多目标问题。所以，