Research On Meiyu Prediction Based On Support Vector Machines

In the past 100 years or more, global climate change increasingly, and kinds of natural disasters come one after another,and summer droughts and floods are one of the major disaster. The information of Meiyu in middle-lower reaches of Yangtze River is major indicators to measure droughts and floods in the middle-lower reaches of Yangtze River in summer. So prediction of Meiyu total is important in studying droughts and floods in the middle-lower reaches of Yangtze River in summer.How to create a prediction model by observing a finite number of historical samples is an important work of economic activity. Statistical learning theory(SLC) focuses on the machine learning theory of small samples. Its core is to control the generalization learning machine by controlling the complexity of models.Supporting vector machine(SVM) is a method of machine learning based on VC dimension and structural risk minimization principle of the statistical learning theory. SVM has advantages in solving small sample size problems in practical applications, such as small sample, nonlinear, over learning, no linear, high dimensional and local minimum point.Time series forecasting is one of the main research topics in intelligent computing. According to recent 106a (1885-1990) data of the Meiyu in middle-lower reaches of Yangtze River and 49a(1954-2002)data of the Meiyu in taizhou, built SVM regression time series model base on Poly and RBF, and used parameter functi-on of grid optimization, Genetic Algorithms(GA),Particle Swarm Optimization(PSO) to optimize the model parameters, and then comparative effectiveness of these six prediction models,and select the best one.