Application of support vector machine to predict precipitation

Empirical risk minimization based neural network suffers drawbacks like over fitting the training data and the choice of the topology structure. According to the periodicity and trend of precipitation, the precipitation forecast model based on support vector machine (SVM) was developed. SVM possesses high generalization ability by employing structural risk minimization to minimize the learning errors and decrease the upper bound of prediction error. Further more, SVM converts machine learning problem into quadratic programming to achieve the global optimal solution. Case study showed that SVM based precipitation prediction model performed significantly better than the BP neural network based model on modeling prediction.