Spatial prediction of nitrate pollution in groundwaters using neural networks and GIS: an application to South Rhodope aquifer (Thrace,Greece)

Neural network techniques combined with Geographical Information Systems (GIS), are used in the spatial prediction of nitrate pollution in groundwaters. Initially, the most important parameters controlling groundwater pollution by nitrates are determined. These include hydraulic conductivity of the aquifer, depth to the aquifer, land uses, soil permeability, and fine to coarse grain ratio in the unsaturated zone. All these parameters were quantified in a GIS environment, and were standardized in a common scale. Subsequently, a neural network classification was applied, using a multi‐layer perceptron classifier with the back propagation (BP) algorithm, in order to categorize the examined area into categories of groundwater nitrate pollution potential. The methodology was applied to South Rhodope aquifer (Thrace, Greece). The calculation was based on information from 214 training sites, which correspond to monitored nitrate concentrations in groundwaters in the area. The predictive accuracy of the model developed reached 86% in the training samples, 74% in the overall sample and 71% in the test samples. This indicates that this methodology is promising to describe the spatial pattern of nitrate pollution. Copyright © 2008 John Wiley & Sons, Ltd.