Use of Artificial Neural Networks for Modelling Cyanobacteria Anabaena spp. in the River Murray, South Australia

Abstract

The use of artificial neural networks (ANNs) for modelling the incidence of cyanobacteria in rivers was investigated by forecasting the occurrence of a species group of Anabaena in the River Murray at Morgan, Australia. The networks of backpropagation type were trained on 7 years of weekly data for eight variables, and their ability to provide a 4-week forecast was evaluated for a 28-week period. They were relatively successful in providing a good forecast of both the incidence and magnitude of a growth peak of the cyanobacteria within the limits required for water quality monitoring. The use of lagged versus unlagged inputs was evaluated in the implementation and performance of the networks. Lagged inputs proved far superior in providing a fit to the actual data. Sensitivity analysis of input variables was performed to evaluate their relative significance in determining the forecast values. The analysis indicated that for this data set for the River Murray, flow and temperature were the predominant variables in determining the onset and duration of cyanobacterial growth. Water colour was the next most important variable in determining the magnitude of the population growth peak. Plant nutrients nitrogen, phosphorus and iron, and turbidity were less important for this time period.