Preface: Optimization for groundwater characterization and management

Datta, Bithin; Kourakos, George

doi:10.1007/s10040-015-1297-3

Cited by 16 publications

(11 citation statements)

References 21 publications

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“…Linked optimization methodologies are widely used for characterization of unknown groundwater contaminated source, in terms of flux magnitude, location and activity history in contaminated aquifers [1] [2]. Transport of contaminants comprising of multiple chemically reactive species, in contaminated groundwater systems, is complex and highly non-linear process.…”

Section: Introductionmentioning

confidence: 99%

Linking a Simulated Annealing Based Optimization Model with PHT3D Simulation Model for Chemically Reactive Transport Processes to Optimally Characterize Unknown Contaminant Sources in a Former Mine Site in Australia

Datta¹,

Petit²,

Palliser³

et al. 2017

JWARP

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Historical mining activities often lead to continuing wide spread contaminants in both groundwater and surface water in previously operational mine site areas. The contamination may continue for many years after closing down the mining activities. The essential first step for sustainable management of groundwater and development of remediation strategies is the unknown contaminant source characterization. In a mining site, there are multiple species of contaminants involving complex geochemical processes. It is difficult to identify the potential sources and pathways incorporating the chemically reactive multiple species of contaminants making the source characterization process more challenging. To address this issue, a reactive transport simulation model PHT3D is linked to a Simulated Annealing based the optimum decision model. The numerical simulation model PHT3D is utilized for numerically simulating the reactive transport process involving multiple species in the former mine site area. The simulation results from the calibrated PHT3D model are illustrated, with and without incorporating the chemical reactions. These comparisons show the utility of using a reactive, geochemical transport process' simulation model. Performance evaluation of the linked simulation optimization methodology is evaluated for a contamination scenario in a former mine site in Queensland, Australia. These performance evaluation results 433illustrate the applicability of linked simulation optimization model to identify the source characteristics while using PHT3D as a numerical reactive chemical species' transport simulation model for the hydro-geochemically complex aquifer study area.

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Section: Introductionmentioning

confidence: 99%

Linking a Simulated Annealing Based Optimization Model with PHT3D Simulation Model for Chemically Reactive Transport Processes to Optimally Characterize Unknown Contaminant Sources in a Former Mine Site in Australia

Datta¹,

Petit²,

Palliser³

et al. 2017

JWARP

Self Cite

View full text Add to dashboard Cite

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“…Moreover, there is less limitation in mathematical definition of objective function and constraints compared to former optimization algorithms such as linear programming [22]. Finally, evolutionary algorithms can optimally identify relatively large number of decision variables [23], and utilization of the evolutionary optimization algorithms simplifies the linking process.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of the evolutionary optimization algorithms include: genetic algorithm (GA) [24], tabu search (TS) [25], simulated annealing (SA), adaptive simulated annealing (ASA) [26], and differential evolution algorithm [27]. Yeh [28] and Datta and Kourakos [22] presented an overview on various optimization methods coupled with simulation techniques utilized for groundwater quantity management, and quality management, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The ill-posed nature of the inverse problem and the plausibility of nonunique solutions can be interrelated as well. More efficient monitoring networks can reduce the plausibility of nonunique solutions, and therefore, optimal monitoring network design is a related issue [9,22]. Also, only if the global optimum solution is found, it may represent the accurate solution to the source identification problem.…”

Section: Introductionmentioning

confidence: 99%

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Application of Simulated Annealing and Adaptive Simulated Annealing in Search for Efficient Optimal Solutions of a Groundwater Contamination related Problem

Amirabdollahian¹,

Datta²

2017

Computational Optimization in Engineering - Paradigms and Applications

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Characterization of groundwater contamination sources is a complex inverse problem. This inverse problem becomes complicated, due to the nonlinear nature of the groundwater flow and transport processes and the associated natural uncertainties. The mathematical challenges arise due to the nonunique characteristics of this problem resulting from the nonunique response of the aquifer system to a set of stresses and the possibility of instead locating only local optimal solutions. The linked simulation-optimization model is an efficient approach to identifying groundwater contamination source characteristics. Efficiency and accuracy of the search for optimum solutions of a linked simulation-optimization depend on the utilized optimization algorithm. This limited study focuses on the application and efficiency of simulated annealing (SA) as the optimization algorithm for solving the source characterization problem. The advantages in using adaptive simulated algorithm (ASA) as an alternative are then evaluated. The possibility of identifying a local optimal solution rather than a global optimal solution when using SA implies failure to solve the source characterization inverse problem. The cost of such inaccurate characterization may be enormous when a remediation strategy is based on the model inferences. ASA is shown to provide a reliable and acceptable alternative for solving this challenging aquifer contamination problem.

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“…Therefore, replacing the numerical simulation models by surrogate models can result in very substantial computational efficiency and feasibility [28]. In the present study, an alternative approach to the linked simulation optimization model and SMBO for optimal characterization of unknown groundwater contaminant sources is proposed and evaluated for potential applicability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

Hazrati-Yadkoori¹,

Datta²

2017

JWARP

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Characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity is a complex problem. In this study, to increase the efficiency and accuracy of source characterization an alternative methodology to the methodologies proposed earlier is developed. This methodology, Adaptive Surrogate Modeling Based Optimization (ASMBO) uses the capabilities of Self Organizing Map (SOM) algorithm to design the surrogate models and adaptive surrogate models for source characterization. The most important advantage of this methodology is its direct utilization for groundwater contaminant characterization without the necessity of utilizing a linked simulation optimization model. The validation of the SOM based surrogate models and SOM based adaptive surrogate models demonstrates that the quantity and quality of initial sample sizes have crucial role on the accuracy of solutions as the designed monitoring locations. The performance evaluation results of the proposed methodology are obtained using error free and erroneous concentration measurement data. These results demonstrate that the developed methodology could approximate groundwater flow and transport simulation models, and substitute the optimization model for characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity.

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Preface: Optimization for groundwater characterization and management

Cited by 16 publications

References 21 publications

Linking a Simulated Annealing Based Optimization Model with PHT3D Simulation Model for Chemically Reactive Transport Processes to Optimally Characterize Unknown Contaminant Sources in a Former Mine Site in Australia

Linking a Simulated Annealing Based Optimization Model with PHT3D Simulation Model for Chemically Reactive Transport Processes to Optimally Characterize Unknown Contaminant Sources in a Former Mine Site in Australia

Application of Simulated Annealing and Adaptive Simulated Annealing in Search for Efficient Optimal Solutions of a Groundwater Contamination related Problem

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

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