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

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  • Liang-Cheng Chang
  • Hone-Jay Chu
  • Chin-Tsai Hsiao

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. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Liang-Cheng Chang & Hone-Jay Chu & Chin-Tsai Hsiao, 2012. "Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(5), pages 1253-1269, March.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:5:p:1253-1269
    DOI: 10.1007/s11269-011-9957-0
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    References listed on IDEAS

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    1. S. Rao & S. Bhallamudi & B. Thandaveswara & V. Sreenivasulu, 2005. "Planning Groundwater Development in Coastal Deltas with Paleo Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(5), pages 625-639, October.
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    Cited by:

    1. Yu-ting Bai & Xiao-yi Wang & Qian Sun & Xue-bo Jin & Xiao-kai Wang & Ting-li Su & Jian-lei Kong, 2019. "Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network," IJERPH, MDPI, vol. 16(20), pages 1-15, October.
    2. Shishir Gaur & Sudheer Ch & Didier Graillot & B. Chahar & D. Kumar, 2013. "Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 927-941, February.
    3. Sina Sadeghfam & Yousef Hassanzadeh & Rahman Khatibi & Ata Allah Nadiri & Marjan Moazamnia, 2019. "Groundwater Remediation through Pump-Treat-Inject Technology Using Optimum Control by Artificial Intelligence (OCAI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1123-1145, February.

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    1. Hone-Jay Chu & Liang-Cheng Chang, 2009. "Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(4), pages 647-660, March.
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