Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Jilin University, Changchun, 130021, China.
Jilin Provincial Key Laboratory of Water Resources and Water Environment, Jilin University, Changchun, 130021, China.
Environ Monit Assess. 2024 Jan 10;196(2):132. doi: 10.1007/s10661-023-12276-5.
In the optimal design of groundwater pollution monitoring network (GPMN), the uncertainty of the simulation model always affects the reliability of the monitoring network design when applying simulation-optimization methods. To address this issue, in the present study, we focused on the uncertainty of the pollution source intensity and hydraulic conductivity. In particular, we utilized simulation-optimization and Monte Carlo methods to determine the optimal layout scheme for monitoring wells under these uncertainty conditions. However, there is often a substantial computational load incurred due to multiple calls to the simulation model. Hence, we employed a back-propagation neural network (BPNN) to develop a surrogate model, which could substantially reduce the computational load. We considered the dynamic pollution plume migration process in the optimal design of the GPMN. Consequently, we formulated a long-term GPMN optimization model under uncertainty conditions with the aim of maximizing the pollution monitoring accuracy for each yearly period. The spatial moment method was used to measure the approximation degree between the pollution plume interpolated for the monitoring network and the actual plume, which could effectively evaluate the superior monitoring accuracy. Traditional methods are easily trapped in local optima when solving the optimization model. To overcome this limitation, we used the grey wolf optimizer (GWO) algorithm. The GWO algorithm has been found to be effective in avoiding local optima and in exploring the search space more effectively, especially when dealing with complex optimization problems. A hypothetical example was designed for evaluating the effectiveness of our method. The results indicated that the BPNN surrogate model could effectively fit the input-output relationship from the simulation model, as well as significantly reduce the computational load. The GWO algorithm effectively solved the optimization model and improved the solution accuracy. The pollution plume distribution in each monitoring yearly period could be accurately characterized by the optimized monitoring network. Thus, combining the simulation-optimization method with the Monte Carlo method effectively addressed the optimal monitoring network design problem under uncertainty.
在地下水污染监测网络（GPMN）的优化设计中，当应用模拟-优化方法时，模拟模型的不确定性会影响监测网络设计的可靠性。针对这一问题，本研究重点研究了污染源强度和水力传导率的不确定性。特别是，我们利用模拟-优化和蒙特卡罗方法，在这些不确定性条件下确定监测井的最佳布局方案。然而，由于多次调用模拟模型，通常会产生大量的计算负担。因此，我们采用了反向传播神经网络（BPNN）来开发一个替代模型，可以大大减少计算负担。我们考虑了 GPMN 优化设计中的动态污染羽流迁移过程。因此，我们制定了一个在不确定性条件下的长期 GPMN 优化模型，旨在最大化每个年度的污染监测精度。空间矩方法用于测量监测网络插值的污染羽流与实际羽流之间的逼近程度，这可以有效地评估卓越的监测精度。传统方法在求解优化模型时容易陷入局部最优解。为了克服这一局限性，我们使用了灰狼优化器（GWO）算法。GWO 算法在避免局部最优解和更有效地探索搜索空间方面被证明是有效的，尤其是在处理复杂的优化问题时。设计了一个假设的例子来评估我们方法的有效性。结果表明，BPNN 替代模型可以有效地拟合来自模拟模型的输入-输出关系，并显著减少计算负担。GWO 算法有效地解决了优化模型，并提高了求解精度。优化监测网络可以准确地描述每个监测年度的污染羽流分布。因此，将模拟-优化方法与蒙特卡罗方法相结合，可以有效地解决不确定性下的最优监测网络设计问题。
