Multi-Objective Optimization of Aquifer Storage and Recovery Operations under Uncertainty via Machine Learning Surrogates
Multi-Objective Optimization of Aquifer Storage and Recovery Operations under Uncertainty via Machine Learning Surrogates
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DOI：
10.1016/j.jhydrol.2022.128299
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发表时间：
2022-08
影响因子：
6.4
通讯作者：
Hamid Vahdat-Aboueshagh;F. Tsai;Emad Elwy Habib;T. Prabhakar Clement
中科院分区：
文献类型：
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作者：
Hamid Vahdat-Aboueshagh;F. Tsai;Emad Elwy Habib;T. Prabhakar Clement
含水层储存和恢复（ASR）是一种重要的水管理方法，将多余的地表水储存到含水层中供以后使用。ASR性能的定量评估不是一项简单的任务，当不确定性分析被添加到问题的维度时，它变得更加精确。将不确定性纳入调度最佳ASR操作的框架也增加了复杂性。本研究整合了替代建模方法，再加上混合整数非线性规划（MINLP）算法，以优化多目标ASR操作。的不确定性进行了分析的基础上，一个彻底的采样的参数空间，以及一个新的分析帕累托前沿和变异函数的代表性的解决方案。选取具有代表性的帕累托前沿拐点进行深入分析。作为一个解决问题的维数，人工神经网络（ANN）生成代理模型预测地下水位和注入水的分布在ASR操作期间的含水层。并行计算的帮助下，克服了在建立大量的人工神经网络和推导出众多的Pareto前沿通过解决MINLP问题的计算复杂性。结果表明，最佳ASR操作高度影响的水力传导系数和纵向分散性。更高的水力传导率值导致在储存和回收阶段期间更高数量的主动应力期，这需要大体积的提取来回收分散的注入物。相反，纵向分散度与水力传导率的较高比率不利地影响注入物采收率。通过变异函数对目标函数不确定性的有意义的表示，可以推断注入水采收率对纵向弥散度比渗透率更敏感。
Aquifer storage and recovery (ASR) is an important water management approach to store excess surface water into aquifers for later use. Quantitative evaluation of ASR performance is not a trivial task and yet becomes more exacting when uncertainty analysis is added to the dimensionality of the problem. Inclusion of uncertainty into the framework of scheduling optimal ASR operations also increases the level of complexity. This study integrates a surrogate modeling approach coupled with a mixed integer nonlinear programming (MINLP) algorithm to optimize multi-objective ASR operations. The uncertainties are analyzed based upon a thorough sampling of the parameters space as well as a novel analysis of Pareto fronts and variograms of representative solutions. Knee point of representative Pareto fronts is selected for in-depth analysis. As a solution to the dimensionality of the problem, Artificial Neural Network (ANN) is employed to generate surrogate models for predicting groundwater levels and injectate distribution within the aquifer during ASR operations. The computational complexity in building a large number of ANNs and deriving of numerous Pareto fronts via solving the MINLP problem are overcome by the assistance of parallel computing. The results show that optimal ASR operations are highly influenced by hydraulic conductivity and longitudinal dispersivity. Higher hydraulic conductivity values lead to a higher number of active stress periods during storage and recovery phases, which requires large volume of extraction to recover the dispersed injectate. In contrast, higher ratios of longitudinal dispersivity to hydraulic conductivity adversely impact the injectate recovery efficiency. Through meaningful representation of objective function uncertainty by variograms, it is inferred that injectate recovery efficiency is more sensitive to longitudinal dispersivity than hydraulic conductivity.