Optimal Remediation Design in Groundwater Systems by Intelligent Techniques

2008

Water Resour Manage

Self Cite

Obtaining optimal solutions for time-varying groundwater remediation design is a challenging task. A novel procedure first employs input/output data sets obtained by constrained differential dynamic programming (CDDP). Then the Adaptive-Network-Based Fuzzy Inference System (ANFIS), which is a fuzzy inference system (FIS) implemented in the adaptive network framework, is applied to acquire time-varying pumping rates. Results demonstrate that the FIS is an efficient way of groundwater remediation design.

Section: Introductionmentioning

confidence: 99%

Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design

Chu

2008

Water Resour Manage

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

Chu

Hsiao

2011

Water Resour Manage

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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.

Optimizing system capacity expansion schedules for groundwater supply

Water Resources Research

Chen

Yeh

2009

Self Cite

[1] Groundwater plays a vital role in regional water resources management. In conventional design, a full-scale network system is installed initially to use groundwater resources. However, the system capacity may exceed water demand in the early stages because water demand generally increases with time. Therefore, this work proposes a novel optimal capacity expansion model capable of determining an optimal schedule to expand system capacity according to increasing water demand. The proposed algorithm hybridizes a genetic algorithm (GA) and constrained differential dynamic programming (CDDP). The chromosomes of the GA represent a possible design alternative, a groundwater network with capacity that expands with time. The CDDP algorithm is then used to compute the optimal pumping policy associated with the chromosome. Simulation results indicate that the capacity expansion model saves more total present value cost than conventional designs for the same annual interest rate and water demand. Results of this study demonstrate promise for the proposed model in facilitating a cost-effective groundwater network design with capacity expansion for regional groundwater supply.