Neural Networks for Agrichemical Vulnerability Assessment of Rural Private Wells

Abstract

Assessment of pesticide and nitrate contamination of rural private wells was conducted using artificial neural networks. Data from 192 drilled and driven wells and 115 large-diameter dug and bored wells, available from two earlier studies, were used for training and testing purposes. Four separate models, two for the two well types and one each for pesticide and nitrate, were developed. Parameters such as depth to aquifer material from land surface, well depth, and distance to cropland were used as input parameters, and the concentrations of nitrate or pesticides were the outputs. While the training efficiency of the network reached between 95 and 100% for these four models, the prediction accuracy for the four models ranged from a low of slightly above 50% for nitrate in dug and bored wells to a high of 90% for pesticides in drilled and driven wells. Sensitivity analyses were performed to examine the impact of network architecture, training and testing parameters, and the size and type of input parameters on model predictions. Multiple hidden layers with a large number of nodes did not appear to have a significant impact on model predictions in two of the four models. The relative importance of input parameters was tested by adding or removing certain key parameters to the model, and it was observed that the parameters had a different impact on drilled/driven versus dug/bored wells.