Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Changchun, 130021, China.
Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, 130021, China.
Environ Sci Pollut Res Int. 2023 Nov;30(53):114535-114555. doi: 10.1007/s11356-023-30228-x. Epub 2023 Oct 20.
The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation-optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution.
地下水污染监测网络（GPMN）的科学布局设计可以提供高质量的地下水监测数据，这对于及时发现和修复地下水污染至关重要。模拟优化方法在获得 GPMN 的最佳设计方面非常有效。蚁群优化（ACO）算法是解决优化模型的有效方法。然而，传统 ACO 算法中使用的参数是通过经验采用固定值的，这可能会影响全局搜索能力和收敛速度。因此，提出了一种基于参数迭代更新策略的蚁群优化（PIUSACO）算法来解决这个问题。对于 GPMN 最优设计问题，应用基于 PIUSACO 算法的模拟-优化框架在中国白城市的一个市政垃圾填埋场中进行了应用。此外，为了在考虑含水层参数和污染源不确定性的情况下降低设计过程的计算负荷，提出了一种遗传算法-支持向量回归（GA-SVR）方法来为数值模型开发代理模型。结果表明，与 ACO 算法和随机布局方案相比，PIUSACO 算法获得的布局方案具有更高的检测率，这表明基于 PIUSACO 算法设计的布局方案能够及时检测到地下水污染的发生。PIUSACO 和传统 ACO 算法的迭代过程比较表明，通过关键参数的迭代更新策略，可以显著提高全局搜索能力并加速收敛速度。本研究证明了 PIUSACO 算法在地下水污染及时检测方面用于 GPMN 最优布局设计的可行性。
