Adaptive surrogate model based optimization (ASMBO) for unknown groundwater contaminant source characterizations using self-organizing maps

Hazrati Yadkoori, Shahrbanoo, and Datta, Bithin (2017) Adaptive surrogate model based optimization (ASMBO) for unknown groundwater contaminant source characterizations using self-organizing maps. Journal of Water Resource and Protection, 9. pp. 193-214.

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Abstract

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.

Item ID: 53069
Item Type: Article (Research - C1)
ISSN: 1945-3108
Keywords: self-organizing map; surrogate models; adaptive surrogate models; groundwater contaminaton; source identificaton
Additional Information:

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

Funders: CRC-CARE Australia
Projects and Grants: CRC-CARE Project No. 5.6.0.3.09/10(2.6.03)
Date Deposited: 16 Apr 2018 01:26
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 100%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 100%
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