Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs

Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine SVM was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir MSR are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate HCO3 − , dissolved oxygen DO , total nitrogen TN , UV254, turbidity, conductivity, nitrate, total nitrogen TN , orthophosphate PO4 3− , total phosphorus TP , suspended solid SS and total organic carbon TOC selected from the correlation analysis of the 23 monthly water variables were included, with 8-year 2001–2008 data for training and the most recent 3 years 2009–2011 for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.