State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China.
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India.
J Contam Hydrol. 2023 May;256:104201. doi: 10.1016/j.jconhyd.2023.104201. Epub 2023 May 8.
We here propose a two-step approach-based simulation-optimization model for multi-objective groundwater remediation using enhanced random vector functional link (ERVFL) and evolutionary marine predator algorithm (EMPA). In this study, groundwater flow and solute transport models are developed using MODFLOW and MT3DMS. The ERVFL network is used to approximate the flow and transport models, enhancing the computational performance. This study also improves the robustness of the ERVFL network using a kernel density estimator (KDE) based weighted least square approach. We further develop the EMPA by modifying the marine predator algorithm (MPA) using elite opposition-based learning, biological evolution operators, and elimination mechanisms. In the multi-objective version of EMPA, the non-dominated/Pareto-optimal solutions are stored in an external repository using an archive controller and adaptive grid mechanism to promote better convergence and diversity of the Pareto front. The proposed methodologies are applied for multi-objective groundwater remediation of a hypothetical unconfined aquifer based on the two-step method. The first step directly integrates flow and transport models with EMPA and finds the optimal locations of pumping wells by minimizing the percent of contaminant mass remaining in the aquifer. In the second step, the ERVL-based proxy model is integrated with EMPA and used for multi-objective optimization while explicitly using the pumping well locations obtained in the first step. The multi-objective optimization generates a Pareto-optimal solution representing the relationship between the rate of pumping and the amount of contaminant mass in the aquifer. Further analyses show a significant advantage of the two-step approach over a traditional method for multi-objective groundwater remediation.
我们在这里提出了一种两步法模拟-优化模型，用于使用增强型随机向量函数链接（ERVFL）和进化海洋捕食者算法（EMPA）进行多目标地下水修复。在本研究中，使用 MODFLOW 和 MT3DMS 开发了地下水流动和溶质运移模型。ERVFL 网络用于逼近流动和传输模型，提高了计算性能。本研究还通过使用基于核密度估计器（KDE）的加权最小二乘方法改进了 ERVFL 网络的稳健性。我们进一步通过使用精英反对学习、生物进化算子和消除机制修改海洋捕食者算法（MPA）来开发 EMPA。在 EMPA 的多目标版本中，使用档案控制器和自适应网格机制将非支配/帕累托最优解存储在外部存储库中，以促进帕累托前沿的更好收敛和多样性。提出的方法应用于基于两步法的假想无限制含水层的多目标地下水修复。第一步直接将流动和传输模型与 EMPA 集成，并通过最小化含水层中剩余污染物质量的百分比来找到最佳的抽水井位置。在第二步中，将基于 ERVL 的代理模型与 EMPA 集成，并在明确使用第一步中获得的抽水井位置的情况下进行多目标优化。多目标优化生成了一个表示抽水井速率与含水层中污染物质量之间关系的帕累托最优解。进一步的分析表明，两步法在多目标地下水修复方面比传统方法具有显著优势。
