Freshwater algal bloom prediction by extreme learning machine in Macau Storage Reservoirs

Abstract

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 in modeling its growth. Recently, extreme learning machine (ELM) was reported to have advantages of only requirement of a small amount of samples, high degree of prediction accuracy and long prediction period to solve the nonlinear problems. In this study, the ELM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir are proposed, in which the water parameters of pH, SiO2, and some other water variables selected from the correlation analysis 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 (based on data on the previous 1st, 2nd, 3rd and 12th months) powers were estimated as approximately 0.83 and 0.90, respectively, showing that the ELM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.