Use of Artificial Intelligence Techniques for the Estimation of Cyanobacteria (Blue-Green Algae) Concentrations

Abstract

This paper presents the development and the evaluation of a modified model formulation for the estimation of algae concentration. The basic structure of the model is that of a typical mechanistic algae growth model. The innovation lies in the use of fuzzy logic for the calculation of the growth rate and, for some scenarios, of the settling rate employed in the mechanistic model. The use of fuzzy logic adds greater flexibility than that offered by standard procedures in the determination of those rates and can help compensate for inadequate data. The results of the comparison of the models with known observations show that all models tested perform somewhat poorly. This may be due to a data set that is not entirely representative of the site under study. Nevertheless, the evaluation shows that the modified mechanistic/fuzzy logic model provides overall estimates that better replicate the mean and standard deviation of the observed algae cell concentrations than those provided by a standard mechanistic model.