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• W2033868873 abstract "This study integrates an artificial neural network (ANN) and constrained differential dynamic programming (CDDP) to search for optimal solutions to a nonlinear time-varying groundwater remediation-planning problem. The proposed model (ANN-CDDP) determines optimal dynamic pumping schemes to minimize operating costs and meet water quality requirements. The model uses two embedded ANNs, including groundwater flow and contaminant transport models, as transition functions to predict groundwater levels and contaminant concentrations under time-varying pumping. Results demonstrate that ANN-CDDP is a simplified management model that requires considerably less computation time to solve a fine mesh problem. For example, the ANN-CDDP computing time for a case involving 364 nodes is 1/26.5 that of the conventional optimization model." @default.
• W2033868873 created "2016-06-24" @default.
• W2033868873 creator A5002956730 @default.
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• W2033868873 date "2011-12-20" @default.
• W2033868873 modified "2023-09-26" @default.
• W2033868873 title "Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management" @default.
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• W2033868873 doi "https://doi.org/10.1007/s11269-011-9957-0" @default.
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