|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|
|Keywords||artificial neural network, coliform concentration, concentration en coliformes, qualité de l'eau, regression, reseau de neurones artificiel, water quality|
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.