Application of artificial neural networks to assess pesticide contamination in shallow groundwater.
In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R=0.87) and pesticide detection efficiency (E=89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination.
Sahoo
Goloka B
GB
Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540, Dole Street, 383 Holmes Hall, Honolulu, HI 96822, USA.
Ray
Chittaranjan
C
Mehnert
Edward
E
Keefer
Donald A
DA
D004784:Environmental Monitoring / Q000379:methods / Q000706:statistics & numerical data; D005618:Fresh Water / Q000032:analysis*; D007087:Illinois; D016571:Neural Networks, Computer; D010575:Pesticides / Q000032:analysis*; D011237:Predictive Value of Tests; D012621:Seasons; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014881:Water Supply / Q000592:standards*
D010575:Pesticides; D014874:Water Pollutants, Chemical