Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran.
Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
J Environ Manage. 2023 Jul 15;338:117842. doi: 10.1016/j.jenvman.2023.117842. Epub 2023 Mar 31.
Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi-objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algorithms- II and III (NSGA-II and NSGA-III), considering uncertainties in the input rates by the conditional value-at-risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.
地下水脆弱性制图在环境管理中至关重要，因为人口增长过多会导致污染增加。然而，据我们所知，很少有研究致力于优化地下水脆弱性模型，考虑风险条件，使用稳健的多目标优化算法并结合多准则决策模型（MCDM）。本研究通过使用三种有区别的模型开发了一种创新的基于风险的多目标优化混合模型来填补这一知识空白。第一个模型基于易感性指数（SI）和 DRASTICA 模型，为两种常见污染物硝酸盐和硫酸盐的相关速率修正生成了两系列情景。第二个模型是一个多目标优化框架，使用非支配排序遗传算法-II 和 III（NSGA-II 和 NSGA-III），考虑到条件风险价值（CVaR）技术输入速率的不确定性。最后，第三个模型是一种著名的多准则决策模型，即复杂比例评估（COPRAS），用于在 Pareto 最优解中识别污染物权重的最佳折衷解。关于硫酸盐的结果，虽然优化后的 DRASTICA 模型导致与初始模型相同的相关性为 0.7，但优化后的 SI 模型将相关性提高到 0.8，而初始模型的相关性为 0.58。对于硝酸盐，优化后的 SI 和 DRASTICA 模型均将相关性提高到 0.6 和 0.7，而初始模型的相关性为 0.36。因此，优化模型之间最佳和最低的相关性分别是 SI 和硫酸盐浓度之间以及 SI 和硝酸盐浓度之间。
