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

Liang Cheng Chang, Hone Jay Chu, Chin Tsai Hsiao

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)1253-1269
頁數17
期刊Water Resources Management
26
發行號5
DOIs
出版狀態Published - 2012 三月

All Science Journal Classification (ASJC) codes

  • 土木與結構工程
  • 水科學與技術

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