A neural network analysis of agrichemical occurrence in groundwater was conducted using data from a pilot study of 192 small-diameter drilled and driven wells and 115 dug and bored wells in Illinois, a regional reconnaissance network of 303 wells across 12 Midwestern states, and a study of 687 domestic wells across Iowa. Potential factors contributing to well contamination (e.g., depth to aquifer material, well depth, and distance to cropland) were investigated. These contributing factors were available in either numeric (actual or categorical) or descriptive (yes or no) format. A method was devised to use the numeric and descriptive values simultaneously. Training of the network was conducted using a standard backpropagation algorithm. Approximately 15% of the data was used for testing. Analysis indicated that training error was quite low for most data. Testing results indicated that it was possible to predict the contamination potential of a well with pesticides. However, predicting the actual level of contamination was more difficult. For pesticide occurrence in drilled and driven wells, the network predictions were good. The performance of the network was poorer for predicting nitrate occurrence in dug and bored wells. Although the data set for Iowa was large, the prediction ability of the trained network was poor, due to descriptive or categorical input parameters, compared with smaller data sets such as that for Illinois, which contained more numeric information.
Use of Qualitative and Quantitative Information in Neural Networks for Assessing Agricultural Chemical Contamination of Domestic Wells
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TECHNICAL PAPERS
Use of Qualitative and Quantitative Information in Neural Networks for Assessing Agricultural Chemical Contamination of Domestic Wells
Abstract
Journal of Geotechnical EngineeringSeptember 1994
Journal of Hydrologic EngineeringApril 2000
Authors
Assistant Professor, Indira Gandhi Inst. of Technology, Saranga, Orissa, India, 759146; formerly, PhD Candidate, Indian Inst. of Technology, Kharagpur, India.
Associate Professor, Civil and Environmental Engineering Department and Water Resources Research Center, Univ. of Hawaii at Manoa, Honolulu, HI 96822 (corresponding author). E-mail: cray@hawaii.edu
Research Hydrologist, U.S. Geological Survey, Iowa City, Iowa 52244.
Received:
Accepted:
Published online: October 15, 2004
Copyright © 2004 ASCE