MODELING CYANOBACTERIA (BLUE-GREEN-ALGAE) IN THE RIVER MURRAY USING ARTIFICIAL NEURAL NETWORKS

In recent times, an apparent increase in the frequency and intensity o f blooms of cyanobacteria (blue-green algae) in the River Murray (Aust ralia) has caused widespread concern. When present in large numbers, t hey can cause serious problems for domestic, industrial, agricultural and recreational users of water, as they can produce toxins and impart undesirable tastes and odours to water. It is important to understand the relationship between the incidence of algal populations and the p revailing environmental conditions in order to prevent algal blooms fr om occurring. In this paper, artificial neural networks (ANNs) are use d to model the incidence of a specific genus of cyanobacteria (Anabaen a sp.) in the River Murray at Morgan, with the dual objectives of fore casting algal concentrations to give prior warning of impending blooms and to identify the factors that affect the blooms of Anabaena. The m odel inputs include weekly values of turbidity, colour, temperature, f low and the concentrations of total nitrogen, as well as soluble and t otal phosphorus. The results obtained are very promising as the model was able to forecast most major variations in Anabaena concentrations (timing and magnitude) for an eight-year period two weeks in advance. A sensitivity analysis carried out on the model inputs indicated that all input variables are important, with no one variable being dominant .