Application of improved core vector machine in the prediction of algal blooms in Tolo Harbour

Support vector machine (SVM) and its derivative algorithms have been increasingly used to predict algal blooms recently. However, its computation complexity remains an annoying problem. To improve the time cost of SVM, a hybrid approach is proposed in this paper based on Partial Least Square (PLS) feature extraction and Core Vector Machine Regression (CVR) algorithm. We describe the principle of our algorithm and the implementation steps in detail. Based on the biweekly data gathered from Tolo Harbour, Hong Kong, some comparative analysis of the performance of PLS-CVR and other algorithms are presented. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably. The experimental results show that our algorithm can reduce the time cost significantly compared to conventional SVM algorithm while maintaining a satisfactory accuracy.