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

Liang-Jeng Chang, Hone Jay Chu*, Chin Tsai Hsiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish
Pages (from-to)1253-1269
Number of pages17
JournalWater Resources Management
Issue number5
StatePublished - 1 Mar 2012


  • Constrained differential dynamic programming
  • Groundwater quality
  • Neural network
  • Optimization


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