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.

含水层储存和回收 (ASR) 是一种重要的水资源管理方法，可将多余的地表水储存到含水层中以备后用。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，它变得更加严格。在调度最优 ASR 操作的框架中包含不确定性也增加了复杂性。本研究将代理建模方法与混合整数非线性规划 (MINLP) 算法相结合，以优化多目标 ASR 操作。基于对参数空间的彻底采样以及对帕累托前沿和代表性解决方案的变异函数的新颖分析，对不确定性进行了分析。选取具有代表性的 Pareto 前沿的拐点进行深入分析。作为解决问题维度的方法，人工神经网络 (ANN) 用于生成替代模型，用于预测 ASR 操作期间含水层内的地下水位和注入分布。并行计算的帮助克服了构建大量人工神经网络和通过解决 MINLP 问题推导大量帕累托前沿的计算复杂性。结果表明，最佳 ASR 操作受水力传导率和纵向分散性的影响很大。较高的水力传导率值会导致在储存和恢复阶段有更多的活动应力期，这需要大量的提取来恢复分散的注入液。相比之下，纵向弥散性与水力传导率的较高比率会对注入液采收率产生不利影响。通过用变差函数有意义地表示目标函数的不确定性，推断注入采收率对纵向弥散性比对水力传导率更敏感。