Optimal control algorithm and neural network for dynamic groundwater management

Hone Jay Chu*, Liang-Jeng Chang

*Corresponding author for this work

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Researchers have found that obtaining optimal solutions for groundwater resource-planning problems, while simultaneously considering time-varying pumping rates, is a challenging task. This study integrates an artificial neural network (ANN) and constrained differential dynamic programming (CDDP) as simulation-optimization model, called ANN-CDDP. Optimal solutions for a groundwater resource-planning problem are determined while simultaneously considering time-varying pumping rates. A trained ANN is used as the transition function to predict ground water table under variable pumping conditions. The results show that the ANN-CDDP reduces computational time by as much as 94-5% when compared to the time required by the conventional model. The proposed optimization model saves a considerable amount of computational time for solving large-scale problems.

Original languageEnglish
Pages (from-to)2765-2773
Number of pages9
JournalHydrological Processes
Volume23
Issue number19
DOIs
StatePublished - 15 Sep 2009

Keywords

  • Constrained differential dynamic programming (CDDP)
  • Groundwater management
  • Neural network

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