Multi-objective optimization of aquifer storage and recovery operations under uncertainty via machine learning surrogates
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Multi-objective optimization of aquifer storage and recovery operations under uncertainty via machine learning surrogates

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  • Journal Title:
    Journal of Hydrology
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    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.
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    Journal of Hydrology, 612, 128299
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    Accepted Manuscript
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