Author(s)

Shahrbanoo Hazrati-Yadkoori^1*, Bithin Datta^1,2

¹Discipline of Civil Engineering, College of Science and Engineering, James Cook University, Townsville, Australia.
²CRC for Contamination Assessment and Remediation of the Environment, CRC CARE, University of Newcastle, Callaghan, Australia.

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.

Self-Organizing Map, Surrogate Models, Adaptive Surrogate Models, Groundwater Contamination, Source Identification

Hazrati-Yadkoori, S. and Datta, B. (2017) Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps. Journal of Water Resource and Protection, 9, 193-214. doi: 10.4236/jwarp.2017.92014.