Modelling algal growth and transport in rivers: a comparison of time series analysis, dynamic mass balance and neural network techniques

Algae present considerable problems for river qualitymanagers and water suppliers and methods to predicttheir behaviour, growth and transport can assist inoperational management. Alternative techniques existfor predicting algal response and three approacheshave been compared and applied to data from six sitesalong the River Thames. These techniques include timeseries analysis, dynamic mass balance and growthequations and neural network approaches. It is shownthat neural network techniques offer a new approachrequiring less intuitive knowledge but predictivecapability is not improved greatly compared to otherapproaches. Neural networks enable models to bedeveloped along all six reaches of the RiverThames.