Long-term prediction model of neural network based on empirical orthogonal functions
Ye Wang · Ziran zaihai xuebao · 2003
Upon using an artificial neural network (ANN) a new short term climate forecast model with the monthly mean rainfall in June in the north of Guangxi as predictand is developed by means of making empirical orthogonal functions (EOF) to the predictors of previous 500hPa height and sea surface temperatures (SSTs), and selecting the high relative principal components. Predictive capability between the new model and linear regression model for the same predictors is discussed based on the independent samples. Evidence suggests that the prognostic ability of the new model with high stability is superior to that of a traditional scheme, due to its condensing the more forecasting information, efficiently utilizing ANN non linear mapping.