A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain

•A hybrid PSO–SVM-based model is built as a predictive model of the cyanotoxin content.•Chlorophyll is a relevant parameter used to estimate the biomass production.•The biological and physical–chemical variables in this process are studied in depth.•The obtained regression accuracy of the hybrid PSO–RBF–SVM-based model is about 95%.•The results show that PSO–SVM-based models can assist in the diagnosis of the cyanotoxin presence.

There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins termed cyanotoxins and, as a result, anticipate its presence is a matter of importance to prevent risks. Cyanobacteria blooms occur frequently and globally in water bodies, and they are a major concern in terms of their effects on other species such as plants, fish and other microorganisms, but especially by the possible acute and chronic effects on human health due to the potential danger from cyanobacterial toxins produced by some of them in recreational or drinking waters. Therefore, the aim of this study is to build a cyanotoxin diagnostic model by using support vector machines (SVMs) in combination with the particle swarm optimization (PSO) technique from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training center of canoeing in the Northern Spain). The Trasona reservoir is near Aviles estuary and after a short tour, the brackish waters of the Aviles estuary empty into the Cantabrian sea. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, cyanotoxin contents have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured water quality parameters (input variables) in the Trasona reservoir (Northern Spain) with success. In other words, the results of the present study are two-fold. In the first place, the significance of each biological and physical–chemical variable on the cyanotoxin content in the reservoir is presented through the model. Second, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the PSO–SVM-based model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.