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Gorissen, D., et al. (2010) A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design. Journal of Machine Learning Research, 11, 2051-2055.

has been cited by the following article:

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

    AUTHORS: Shahrbanoo Hazrati-Yadkoori, Bithin Datta

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

    JOURNAL NAME: Journal of Water Resource and Protection, Vol.9 No.2, February 14, 2017

    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.