Discipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, QLD, 4811, Australia.
Discipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, QLD, 4811, Australia.
J Environ Manage. 2019 Mar 15;234:115-130. doi: 10.1016/j.jenvman.2018.12.054. Epub 2019 Jan 4.
To date, simulation-optimization (S/O) based groundwater management models have delivered optimal saltwater intrusion management strategies for coastal aquifer systems. At times, however, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management model. The present study explicitly incorporated aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer situated in a small Pacific island country. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from the barrier wells (hydraulic barriers) to ensure that the water quality at different monitoring locations (MLs) were within pre-specified sustainable limits. To achieve the targeted management goal, a coupled flow and transport numerical simulation model of the Bonriki aquifer was developed using the FEMWATER numerical code. The developed three-dimensional numerical model was calibrated and validated using limited available hydrological data. To achieve computational efficiency and feasibility of the management model, the numerical simulation model in the S/O model was replaced with ensembles of Support Vector Machine Regression (SVMR) surrogate models. Each SVMR standalone surrogate model in the ensemble was constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the multi-objective genetic algorithm optimization model to solve the Bonriki aquifer management problem. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model established after validation of the optimal solution results suggests that the implemented constraints of the optimization problem were satisfied, i.e., the salinity concentrations at respective MLs were within the pre-specified limits. Overall, the results from this study indicated that the developed management model has the potential to address groundwater salinity problems in small island countries.
迄今为止，基于仿真优化（S/O）的地下水管理模型已经为沿海含水层系统提供了最佳的海水入侵管理策略。然而，由于含水层参数的不确定性，数值模拟模型中的不确定性有时并未纳入管理模型中。本研究明确将含水层参数不确定性纳入多目标管理模型中，以优化位于太平洋小岛国的无约束邦里基含水层的地下水抽取策略的设计。多目标管理模型的目标是最大限度地从生产井中抽取，同时最小化从屏障井（水力屏障）中抽取，以确保不同监测点（ML）的水质在预定的可持续范围内。为了实现目标管理目标，使用 FEMWATER 数值代码开发了邦里基含水层的耦合流动和传输数值模拟模型。所开发的三维数值模型使用有限的可用水文数据进行了校准和验证。为了实现管理模型的计算效率和可行性，S/O 模型中的数值模拟模型被支持向量机回归（SVMR）代理模型的集合所取代。集合中的每个 SVMR 独立代理模型都是使用具有不同渗透率和孔隙率值的不同数值模拟模型的数据集构建的。这些集合 SVMR 模型与多目标遗传算法优化模型耦合，以解决邦里基含水层管理问题。执行的优化模型呈现出具有 600 个非支配最优权衡抽取解决方案的 Pareto 前沿。经优化解决方案结果验证后建立的管理模型的可靠性表明，优化问题的实施约束得到了满足，即各自 ML 的盐度浓度在预定范围内。总体而言，这项研究的结果表明，所开发的管理模型有可能解决小岛屿国家的地下水盐分问题。
