SELF-ORGANIZING MAP BASED SURROGATE MODELS FOR CONTAMINANT SOURCE IDENTIFICATION UNDER PARAMETER UNCERTAINTY

Abstract

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.