Using Geostatistics and Artificial Neural Networks to Determine the Location of a Contaminant Source

摘要

Accurate detection of a contaminant source is vital to the management of groundwater restoration projects. We developed a technique for identifying the contaminant source areas that combines site data, geostatistics and artificial neural networks (ANNs). The methodology involves replicating the kriging methods with ANNs to provide estimates of spatially dependent concentration fields, and for quantifying the uncertainty associated with the estimates. Once trained the ANNs approximate the results of ordinary kriging in a more computationally efficient manner. This provides a bi-directional prediction (i.e. forecast the future plume, as well as, the original source), which is impossible for many physics-based models.