Predicting near-shore coliform bacteria concentrations using ANNS.
Details are given of the application of Artificial Neural Networks (ANNs) to predicting the compliance of bathing waters along the coastline of the Firth of Clyde, situated in the south west of Scotland, UK. Water quality data collected at 7 locations during 1990-2000 were used to set up the neural networks. In this study faecal coliforms were used as a water quality indicator, i.e. output, and rainfall, river discharge, sunlight and tidal condition were used as input of these networks. In general, river discharge and tidal ranges were found to be the most important parameters that affect the coliform concentration levels. For compliance points close to the meteorological station, the influence of rainfall was found to be relatively significant to the concentration levels.
Lin
B
B
School of Engineering, Cardiff University, PO Box 925, Cardiff CF24 0YF, UK. linbl@cardiff.ac.uk
Kashefipour
S M
SM
Falconer
R A
RA
D004755:Enterobacteriaceae / Q000254:growth & development*; D005544:Forecasting; D016571:Neural Networks, Computer; D011891:Rain; D011998:Recreation; D012606:Scotland; D014871:Water Microbiology; D014872:Water Movements; D014873:Water Pollutants
D014873:Water Pollutants