Integrating Support Vector Regression with Particle Swarm Optimization for Numerical Modeling for Algal Blooms of Freshwater

Abstract

Algae-releasing cyanotoxins are cancer causing and very harmful to the human being. Therefore, it is of great significance to model how the algae population dynamically changes in freshwater reservoirs. But the practical modeling is very difficult because water variables and their internal mechanism are very complicated and nonlinear. So, in order to alleviate the algal bloom problems in the Macau Main Storage Reservoir (MSR), this work proposes and develops a hybrid intelligent model combining support vector regression (SVR) and particle swarm optimization (PSO) to yield optimal control of parameters that predict and forecast the phytoplankton dynamics. In this process, collected data for current months’ variables and previous months’ variables are used for the model to predict and forecast, respectively. In the correlation analysis of 23 water variables that were monitored monthly, 15 variables such as alkalinity, bicarbonate (HCO3−), dissolved oxygen (DO), total nitrogen (TN), turbidity, conductivity, nitrate, suspended solid (SS), and total organic carbon (TOC) are selected, and data from 2001 to 2008 for each of these selected variables are used for training, while data from 2009 to 2011 which are the most recent 3 years are used for testing. It can be seen from the numerical results that the prediction and forecast powers are, respectively, estimated at approximately 0.767 and 0.876, and naturally it can be concluded that the newly proposed PSO–SVR is working well and can be adopted for further studies.