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Water Resources Management

Erschienen in:

01.03.2012

Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management

verfasst von: Liang-Cheng Chang, Hone-Jay Chu, Chin-Tsai Hsiao

Erschienen in: Water Resources Management | Ausgabe 5/2012

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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.

Vorheriger Artikel Variations in Discharge Volumes for Hydropower Generation in Switzerland

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Titel: Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management
verfasst von: Liang-Cheng Chang
Hone-Jay Chu
Chin-Tsai Hsiao
Publikationsdatum: 01.03.2012
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 5/2012
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-011-9957-0

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