Predicting near-shore coliform bacteria concentrations using ANNS.

B Lin; S M Kashefipour; R A Falconer

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PubMed · 15137174

Predicting near-shore coliform bacteria concentrations using ANNS.

B Lin·S M Kashefipour·R A Falconer

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Abstract

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.

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Enterobacteriaceae Forecasting Neural Networks, Computer Rain Recreation Scotland Water Microbiology Water Movements Water Pollutants Journal Article Research Support, Non-U.S. Gov't

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B Lin, S M Kashefipour, R A Falconer. 2003. Predicting near-shore coliform bacteria concentrations using ANNS.. https://pubmed.ncbi.nlm.nih.gov/15137174/

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