Use of neural networks as a tool to assess pesticide contamination potential of rural domestic wells

Chittaranjan Ray, Kristopher K. Klindworth

Research output: Contribution to conferencePaperpeer-review

Abstract

Artificial Neural Networks were used to assess nitrate and pesticide contamination potential of rural private wells using data from 192 drilled and driven wells and 163 large-diameter dug and bored wells. Four separate models, two for the two well types and one each for pesticide and nitrate, were developed for training and testing. Input parameters were fed as discrete values belonging to various ranges of values (like that represented in a histogram format) and the output was a set of four values with high, medium, low, or no impacts. While the training efficiency of the network reached between 95 and 100 percent for these four models, the prediction accuracy for the four models ranged from a low of slightly above 50 percent for nitrate in dug and bored wells to a high of 90 percent for pesticides in drilled and driven wells. The initial results of this study indicates that ANN may be potential screening tool to assess well contamination.

Original languageEnglish (US)
Pages973-978
Number of pages6
StatePublished - 1998
Externally publishedYes
EventProceedings of the 1998 International Water Resources Engineering Conference. Part 2 (of 2) - Memphis, TN, USA
Duration: Aug 3 1998Aug 7 1998

Conference

ConferenceProceedings of the 1998 International Water Resources Engineering Conference. Part 2 (of 2)
CityMemphis, TN, USA
Period8/3/988/7/98

ASJC Scopus subject areas

  • General Earth and Planetary Sciences
  • General Environmental Science

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