Identification of unknown groundwater pollution sources using classical optimization with linked simulation

Datta, Bithin; Chakrabarty, Dibakar; Dhar, Anirban

doi:10.1016/j.jher.2010.08.004

Cited by 97 publications

(23 citation statements)

References 43 publications

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“…This model grid was taken and modified from Datta, Chakrabarty and Dhar [46] and Singh and Datta [49]. The study area was assumed to be homogeneous and isotropic.…”

Section: Hypothetical Scenariosmentioning

confidence: 99%

Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Huang

Wang

Zhang

et al. 2018

Water

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Prevention and remediation strategies for groundwater pollution can be successfully carried out if the location, concentration, and release history of contaminants can be accurately identified. This, however, presents a challenge due to complex groundwater systems. To address this issue, a simulation-optimization (S/O) model by integrating MODFLOW and MT3DMS into a shuffled complex evolution (SCE-UA) optimization algorithm was proposed; this coupled model can identify the unknown groundwater pollution source characteristics. Moreover, the Grids Traversal algorithm was used for automatically searching all possible combinations of pollution source location. The performance of the proposed S/O model was tested by three hypothetical scenarios with various combinations of mixed situations (i.e., single and multiple pollution source locations, known and unknown pollution source locations, steady-state flow and transient flow). The field measurement errors was additionally considered and analyzed. Our results showed that this proposed S/O model performed reasonably well. The identified locations and concentrations of contaminants fairly matched with the imposed inputs with average normalized deviations less than 1% after sufficient generations. We further assessed the impact of generation number on the performance of the S/O model. The performance could be significantly improved by increasing generation number, which yet resulted in a heavy computational burden. Furthermore, the proposed S/O model performed more efficiently and robustly than the traditionally used artificial neural network (ANN)-based model. This is due to the internal linkage of numerical simulation in the S/O model that promotes the data exchange from external files to programming variables. This new model allows for solving the source-identification problems considering complex conditions, and thus for providing a platform for groundwater pollution prevention and management.

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“…This model grid was taken and modified from Datta, Chakrabarty and Dhar [46] and Singh and Datta [49]. The study area was assumed to be homogeneous and isotropic.…”

Section: Hypothetical Scenariosmentioning

confidence: 99%

Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Huang

Wang

Zhang

et al. 2018

Water

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“…In this method the optimization models are linked with the flow and transport simulation models [4][5][6][7][8][9][10]. The optimal source characteristics are achieved when the difference between estimated and observed contaminant concentrations at monitoring locations is International Journal of GEOMATE, Sept., 2016, Vol.…”

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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“…Wagner (1995) discusses the historical perspective and the future prospects of using the linked simulation-optimization approach in various types of groundwater management models, as well as source identification and parameter estimation aspects. However, as noted in Datta et al (2011), the dimensionality of the optimal search problem increases exponentially with the increase in number of decision variables. The number of iterations between the optimization algorithm and the externally linked simulation models required to obtain an optimal or near optimal solution also increases exponentially as the number of decision variables increases.…”

Section: Introductionmentioning

confidence: 99%

Preface: Optimization for groundwater characterization and management

Datta

Kourakos

2015

Hydrogeol J

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Identification of unknown groundwater pollution sources using classical optimization with linked simulation

Cited by 97 publications

References 43 publications

Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Groundwater Contamination Plume Delineation Using Local Singularity Mapping Technique

Preface: Optimization for groundwater characterization and management

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