A new predictive model for the cyanotoxin content from experimental cyanobacteria concentrations in a reservoir based on the ABC optimized support vector machine approach: A case study in Northern Spain

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in surface waters used for drinking or for recreation. Consequently, anticipation of its presence is a matter of importance to prevent risks. The aim of this study is to build a cyanotoxin diagnostic model by using support vector machines (SVMs) in combination with the artificial bee colony (ABC) technique from cyanobacterial concentrations determined experimentally in the Trasona reservoir (Northern Spain), to forecast the cyanotoxins' presence in the Trasona reservoir (Northern Spain). The ABC-SVM model is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out have proven its high performance. The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the cyanotoxin content in the reservoir is presented through the model. Secondly, a predictive model of the cyanotoxin content is obtained. The agreement of the ABC-SVM-based model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.