Optimal planning of a dynamic pump-treat-inject groundwater remediation system

Chang, Liang-Cheng; Chu, Hsin; Hsiao, Chin Tsai

doi:10.1016/j.jhydrol.2007.05.030

Cited by 50 publications

(33 citation statements)

References 20 publications

(34 reference statements)

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“…In the simulationoptimization model, the surface and subsurface water simulation models including the LP and ANN are embedded in the genetic algorithm (GA) to determine the pumping rate and water allocation in each time step sequentially. For the details of GA refer to McKinney and Lin [35], Chen et al [36], and Chang et al [37]. The result reveals that 10-day and annual SI of the FIS and simulation-optimization model (Cases 2 and 3) are similar (Table 4).…”

Section: Comparison Of Fis and Simulation-optimization Modelmentioning

confidence: 82%

A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water

Chang

Chu

Chen

2013

Advances in Fuzzy Systems

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This study develops the water resources management model for conjunctive use of surface and subsurface water using a fuzzy inference system (FIS). The study applies the FIS to allocate the demands of surface and subsurface water. Subsequently, water allocations in the surface water system are simulated by using linear programming techniques, and the responses of subsurface water system with respect to pumping are forecasted by using artificial neural networks. The operating rule for the water systems is that the more abundant water system supplies more water. By using the fuzzy rule, the FIS conjunctive use model easily incorporates expert knowledge and operational polices into water resources management. The result indicates that the FIS model is more effective and efficient when compared with the decoupled conjunctive use and simulation-optimization models. Furthermore, the FIS model is an alternative way to obtain the conjunctive use policies between surface and subsurface water.

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Section: Comparison Of Fis and Simulation-optimization Modelmentioning

confidence: 82%

A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water

Chang

Chu

Chen

2013

Advances in Fuzzy Systems

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“…Furthermore, containing the contamination by removing contaminated ground water should be a dynamic process. Dynamic policies which allow changing pumping policies as the contaminant plume moves, would expectedly be more cost-effective than static policies (Chang et al 1992(Chang et al , 2007.The optimal remediation system is cost-effective and the timevarying operation policy is allowed to change as the contaminant plume moves.…”

Section: Case1: Comparison Of the Results Of Various Pumping Wellsmentioning

confidence: 99%

“…Quadratic programming is applied to solve the series of quadratic sub-problems for t01…T, forward in time, and obtains the optimal policy. Notably, the computed optimal policy becomes the nominal policy for the next iteration Chang et al 2007). Since a quadratic problem is only an approximation of the original problem, iterations are required.…”

Section: Integration Of Cddp and Annmentioning

confidence: 99%

“…The P&T method is primarily common and useful for decontaminating groundwater with highly soluble pollutants by pumping out and treating contaminated groundwater (Chu and Chang 2009b). Several studies have investigated the feasibility of coupling optimization technique with groundwater flow and transporting simulation to design P&T systems (Chang et al 1992;McKinney and Lin 1994;Wang and Zheng 1998;Chang and Hsiao 2002;Chu et al 2005;Chang et al 2007). Significant advances over the past two decades apply optimized ground water management (Gorelick et al 1984;Jones et al 1987;Yeh 1992;Ahlfeld et al 1988;McKinney and Lin 1994;Chang and Hsiao 2002;Hsiao and Chang 2002;Hsiao and Chang 2005;Chu and Chang 2010).…”

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confidence: 99%

“…Remediation systems require a dynamic simulation or nonlinear optimization model in order to yield satisfactory results, unless the input assumptions justify a static system (Chang et al 2007). Applying an optimization technique such as GA and simulated annealing (SA) to solve time-varying policies would dramatically increase required computational resources (Mckinney and Lin 1994;Wang and Zheng 1998;Rao et al 2003).…”

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confidence: 99%

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

Chang

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.

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Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.

Sahoo

Russo

Elliott

et al. 2017

Water Resources Research

301

121

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Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross‐validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003–2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.

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Optimal planning of a dynamic pump-treat-inject groundwater remediation system

Cited by 50 publications

References 20 publications

A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water

A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water

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

Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S.

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