Title
Research on Water Bloom Prediction Based on Least Squares Support Vector Machine
Abstract
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.
Year
DOI
Venue
2009
10.1109/CSIE.2009.476
CSIE (5)
Keywords
Field
DocType
rough set theory,least squares,prediction accuracy,artificial neural network prediction,long prediction period,medium-term water bloom prediction,artificial neural network prediction model,high prediction accuracy,rivers,water bloom,intelligent prediction model,vector machine,water bloom prediction,least squares approximations,algorithm,support vector machine,simulation,long-term prediction period,squares support,squares support machine,support vector machines,squares support vector machine,least squares support vector machine,water pollution,artificial neural networks,artificial neural network,predictive models,prediction model
Least squares,Data mining,Least squares support vector machine,Bloom,Computer science,Support vector machine,Rough set,Artificial intelligence,Artificial neural network,Machine learning
Conference
Volume
ISBN
Citations 
5
978-0-7695-3507-4
2
PageRank 
References 
Authors
0.41
3
5
Name
Order
Citations
PageRank
Zaiwen Liu177.23
Xiaoyi Wang220.75
Lifeng Cui341.50
Xiaofeng Lian421.76
Jiping Xu535.50