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 language | English |
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Pages (from-to) | 2765-2773 |
Number of pages | 9 |
Journal | Hydrological Processes |
Volume | 23 |
Issue number | 19 |
DOIs | |
State | Published - 15 Sep 2009 |
Keywords
- Constrained differential dynamic programming (CDDP)
- Groundwater management
- Neural network