IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v26y2012i5p1253-1269.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-011-9957-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-011-9957-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. R. Rejani & Madan Jha & S. Panda & R. Mull, 2008. "Simulation Modeling for Efficient Groundwater Management in Balasore Coastal Basin, India," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 23-50, January.
    2. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:26:y:2012:i:5:p:1253-1269. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.