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Utilizing Artificial Neural Networks to backtrack source location

Preprint, 2006


Status: Published

Citations:

Cite: [bibtex]


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Abstract: Determining the location of the contaminant source is important for improving remediation and site management decisions at many contaminated groundwater sites. At large sites, numerical flow and transport models have been developed that use historical measurement data for calibration. A well-calibrated model is useful for predicting plume migration and other management purposes; however, it is difficult to back out the source with these forward flow and transport models. We present a novel technique utilizing Artificial Neural Networks (ANNs) to backtrack source location and earlier plume concentrations from recent plume information. For proof-of-concept, two tracer tests (a non-point-source and a point-source) were performed in a large-scale (10'×14'×6') groundwater physical model. The physics-based flow and transport model (MODFLOW 2000 and MT3DMS) was calibrated using the data from the non-point-source tracer test and validated using the point source tracer test data. ANNs (e.g. counterpropagation) were trained using the calibrated model predictions and compared to actual data. Results show this to be a promising method for determining earlier plume and source locations.



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