Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

Amirabdollahian, Mahsa; Datta, Bithin

doi:10.7763/ijesd.2015.v6.647

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…These approaches include: embedded optimization method [1], [2] and linked simulation optimization method which is the most effective approach to contaminant source identification. In linked simulation optimization approach different optimization algorithms were utilized such as Genetic Algorithm (GA) [3], [4], Simulated Annealing (SA) [5] and Adaptive Simulated Annealing (ASA) [4], [6]. Only a few of previously developed methodologies such as [4], [6] were evaluated under uncertain hydrogeological parameter conditions.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Map Based Surrogate Models for Contaminant Source Identification Under Parameter Uncertainty

Y.¹,

Datta²

2017

GEOMATE

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Identification of unknown groundwater contaminant sources is a complex problem. The complexities arise mainly due to the uncertainties related to the hydrogeologic information, sparsity of measurement data and unavoidable concentration measurement errors. The process of contaminant source identification with sparse and limited concentration measurement data especially when the hydrogeologic parameters are uncertain requires an efficient procedure. The existing methodologies to tackle this problem in real world cases usually require huge computational time and the solutions may be non-unique. The goal of this study is to evaluate a developed methodology to characterize the groundwater contamination sources in a heterogeneous, multi layered aquifer. This developed methodology utilizes the Self Organizing Maps (SOM) algorithm to design the surrogate models for source characterization. The most important advantages is that in this methodology, the trained SOM based surrogate models is directly utilized for groundwater contaminant source characterization without the necessity of using a separate linked simulation optimization model. The performance of the developed methodology is evaluated by using deterministic hydraulic conductivity values, and uncertain hydraulic conductivity values. These results indicate that the developed methodology could efficiently approximate groundwater flow and transport simulation models, and also characterize unknown groundwater contaminant sources in terms of location, magnitude and release history.

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Section: Introductionmentioning

confidence: 99%

Self-Organizing Map Based Surrogate Models for Contaminant Source Identification Under Parameter Uncertainty

Y.¹,

Datta²

2017

GEOMATE

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“…The linked simulation optimization procedures consist of two main components: 1) models for simulation of groundwater flow and contaminant transport processes, 2) optimization model with an optimization algorithm. Some of the optimization algorithms utilized are linear programming and multiple regressions technique [7]; a nonlinear optimization model with embedding technique [8] [9] [10]; Genetic Algorithm (GA) [11] and [12]; the Artificial Neural Network (ANN) [13] and [14]; a hybrid methodology based on GA [15] and [16]; the classical nonlinear optimization algorithm [17]; Simulated Annealing (SA) [18] [19] [20] [21] and Adaptive Simulated Annealing (ASA) [22], Genetic Programming (GP) [23] and [24]; ASA in conjunction with uncertainty modeling [25] and [26]. Application of these methodologies to real-world cases is generally computationally time intensive, and may need days or weeks of CPU time to obtain an optimal solution.…”

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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“…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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Reliability Evaluation of Groundwater Contamination Source Characterization under Uncertain Flow Field

Cited by 4 publications

References 10 publications

Self-Organizing Map Based Surrogate Models for Contaminant Source Identification Under Parameter Uncertainty

Self-Organizing Map Based Surrogate Models for Contaminant Source Identification Under Parameter Uncertainty

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

Groundwater Contamination Plume Delineation Using Local Singularity Mapping Technique

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