CONTAMINANT SOURCE IDENTIFICATION USING INVERSE PROCEDURE

Abstract

Groundwater is the key source of drinking water that is necessary to sustain life on earth. Identifying contaminant sources in groundwater is important for developing effective remediation strategies and identifying responsible parties in a contamination incident. Groundwater source identification problems require solution of an inverse problem. In the present study, contaminant source distance is estimated by using the Genetic Algorithm optimization (GA). A numerical model (Advection- Dispersion equation) is used to find the contaminant concentration. The source distance estimation has been formulated as a least-squares optimization problem by minimizing the deviations between the observed contaminant concentration and the computed concentration. The bias induced by three objective functions was statistically analyzed by generating synthetic concentration data. It has been observed that, when the concentration data contain no errors, the objective functions do not induce any bias in the source distance estimation and the true source distance is uniquely identified. However, in the presence of noise, these objective functions induce bias in the source distance estimation. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed concentration data has resulted in the best possible estimates.