Identification of Pollutant Source Characteristics Under Uncertainty in Contaminated Water Resources Systems Using Adaptive Simulated Anealing and Fuzzy Logic

Amirabdollahian, Mahsa; Datta, Bithin

doi:10.21660/2014.11.3258

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…The Normalized Absolute Error of Estimation (NAEE%), computed using Eq. (8), is utilized to quantify the error in the estimated source fluxes. are the estimated and actual source fluxes for stress period ii, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

Amirabdollahian¹,

Datta²

2015

IJESD

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Abstract-Groundwater contamination is one of the serious environmental problems. Effective remediation strategies require accurate characteristics of contamination sources. Contamination source identification approaches need accurate flow and contaminant transport simulation models. In order to obtain reliable solutions, the simulation models need to be provided with reliable hydrogeologic information. In real life scenarios usually sparse and limited hydrogeologic information is available. In this study two hydraulic conductivity sampling networks are ranked based on their effectiveness in identifying reliable contamination source characteristics. Using multiple realizations of hydraulic conductivity fields, and the location and size of the contaminant plume at different monitoring stages, an index of reliability is estimated for each hydraulic conductivity sampling network. It is demonstrated that the source characteristics identified by utilizing the sampling network with higher index of reliability results in more accurate characterization of contamination sources. Therefore the developed methodology provides a tool to select an appropriate hydrogeologic sampling network for more efficient characterizing of contamination sources.

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Section: Resultsmentioning

confidence: 99%

“…The optimization is constrained by the relative error in the estimation of total mass over all grid points at which the interpolation was performed. Amirabdollahian and Datta [8] studied the effect of hydrogeologic parameter value uncertainty in optimal characterization of contamination sources.…”

Section: Introductionmentioning

confidence: 99%

Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

Amirabdollahian¹,

Datta²

2015

IJESD

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“…Finally, evolutionary algorithms can optimally identify relatively large number of decision variables [23], and utilization of the evolutionary optimization algorithms simplifies the linking process. 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%

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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“…The other approach involves directly delineating the contamination plume using available observed concentrations. The interpolation techniques such as Kriging [14,16,22], and Inverse Distance Weighting method [23,24] are two of these methods. The accuracy of the interpolation methods depends on the values selected for the interpolation parameters.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Contamination Plume Delineation Using Local Singularity Mapping Technique

Datta¹,

Amirabdollahian²,

Prakash³

2016

Geomate

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ABSTRACT:The evaluation and remediation of contaminated aquifers require accurate delineation of contamination plumes. Ideally a large number of observed concentration data are required to achieve an accurate delineation of the contamination plume. However, in practice due to the budgetary constraints, the contamination in groundwater resources is detected by limited number of arbitrary located or predesigned contamination monitoring wells. Therefore, a technique is required to estimate the boundaries of the plume using the available sparse observation data. In this work, Local Singularity Mapping Technique is used for plume delineation. The singularity mapping technique is based on the multifractal concept. In fractal geometry a local feature is similar to the whole in terms of shape and structure. Generalized self-similarity is characterized by a power-law relationship. Using this method, singularity indices are estimated for the entire study area using sampled concentration data. According to these indices the mapped area (study area) is classified into subsets including contaminated and clean areas. The boundaries between these two subset areas can be identified as the contamination plume edge. The performance of this method is evaluated in an illustrative contaminated study area to demonstrate the potential applicability of the proposed methodology. The singularity indices can be utilized to locate potential contamination sources as well as plume boundaries. These evaluation results demonstrated that the contamination plumes can be relatively accurately delineated using the fractal geometry.

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Identification of Pollutant Source Characteristics Under Uncertainty in Contaminated Water Resources Systems Using Adaptive Simulated Anealing and Fuzzy Logic

Cited by 12 publications

References 8 publications

Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

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

Groundwater Contamination Plume Delineation Using Local Singularity Mapping Technique

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