Research on Water Bloom Prediction Based on Least Squares Support Vector Machine
Zaiwen Liu, Xiaoyi Wang, Lifeng Cui, Xiaofeng Lian, Jiping Xu · 2009
An intelligent prediction model for water bloom of rivers and lakes based on least squares support vector machine (LSSVM) is proposed, in which main influence factor of outbreak of water bloom is analyzed by rough set theory first, and this model is compared with artificial neural network prediction model. The comparison result indicates: in the aspect of medium-term water bloom prediction in rivers and lakes, the accuracy of prediction with least squares support machine is higher than that of artificial neural network. Least squares support machine, which has long prediction period and high degree of prediction accuracy, needs a small amount of sample and can predict the medium-term change discipline of chlorophyll well. The results of simulation and application show that: LSSVM improves the algorithm of support vector machine (SVM)iquest it has long-term prediction period, strong generalization ability and high prediction accuracy; and this model provides an efficient new way for medium-term water bloom prediction.