RESUMO
We investigated the relationship between some water quality parameters and microcystin, chlorophyll-a, and cyanobacteria in different conditions of water temperature. We also proposed to predict chlorophyll-a concentration in the Billings Reservoir using three machine learning techniques. Our results indicate that in the condition of higher water temperatures with high density of cyanobacteria, microcystin concentration can increase severely (>102 µg/L). Besides the magnitude observed in higher concentrations, in water temperatures above 25.3 °C (classified as high extreme event), higher frequencies of inadequate values of microcystin (87.5%), chlorophyll-a (70%), and cyanobacteria (82.5%) compared to cooler temperatures (<19.6 °C) were observed. The prediction of chlorophyll-a in Billings Reservoir presented good results (0.76 ≤ R2 ≤ 0.82; 0.17 ≤ RMSE≤0.20) using water temperature, total phosphorus, and cyanobacteria as predictors, with the best result using Support Vector Machine.
