Forecasting the cyanotoxins presence in fresh waters: A new model based on genetic algorithms combined with the MARS technique

Abstract

Cyanobacteria are one of the major concerns to public health since some of them produce a range of potent toxins (cyanotoxins). This group of microorganism can be present in drinking and recreation waters representing a health risk for animals and human being. For this reason, as prevention, it is important to bring forward their presence. In this study, using physical–chemical and biological parameters, a hybrid approach based on genetic algorithms (GAs) combined with the multivariative adaptative regression splines (MARS) technique, was developed and applied for forecasting the presence of cyanobacteria in a water reservoir (Trasona reservoir, Northern Spain) and in consequence, the cyanotoxin risk. The significance of each biological and physical–chemical variables used for its determination was assessed and a predictive model useful for preventing the presence of cyanobacteria, and consequently of cyanotoxins, was defined.