Title | Comparing artificial neural networks and regression models for predicting faecal coliform concentrations |
Publication Type | Journal Article |
Year of Publication | 2007 |
Authors | Mas DML, Ahlfeld D. P. |
Journal | Hydrological Sciences Journal |
Volume | 52 |
Issue | 4 |
Start Page | 713 |
Pagination | 713-731 |
Date Published | 08/2007 |
Keywords | artificial neural network, coliform concentration, concentration en coliformes, qualité de l'eau, regression, reseau de neurones artificiel, water quality |
Abstract | This paper compares the performance of ordinary least squares (OLS) and binary logistic regression methods, and artificial neural networks (ANNs) for the prediction of surface water faecal coliform concentrations in a 8.2 km2 mixed land-use watershed. Model inputs consist of precipitation and temperature data, as well as instantaneous measurements of streamflow and conductivity. The ANNs are able to correctly classify 69% and 85% of faecal coliform concentrations relative to 20 and 200 cfu/100 mL water quality standards, respectively, results moderately better than those observed for the regression models. The ANN models using only meteorological inputs were able to correctly classify 72% and 81% of the observations relative to the 20 and 200 cfu/100 mL standards, respectively. The ANN models are notably better at predicting when the 200 cfu/100 mL standard is violated. In addition, the ANN models have lower percentages of false negatives, a characteristic desirable for protection of public health. |
DOI | 10.1623/hysj.52.4.713 |