The Effect of Model Input on Forecast Results of the Neural Network Ensemble Model
A new calculation method for the input of the neural network ensemble prediction (NNEP) model has been developed based on the data mining technology using the feature extraction method of Empirical Orthogonal Function (EOF) and the stepwise regression method, for investigating the effect of different model input with the same dimension on the prediction capacity of the NNEP model. Taking typhoon intensity in summer (June, July and August) in the Northwest Pacific in China as the prediction object, a new NNEP model for typhoon intensity was established. Using identical sample cases and input dimension, predictions of typhoon intensity with multi-model and large sample size were performed. Results show that the methodology of EOF combined with stepwise regression method can mine the useful prediction information from all the predictors, so the prediction accuracy of the NNEP model is clearly improved.
neural network typhoon intensity ensemble prediction feature extraction
Long Jin Ying Huang Hui Yu Xiaoyan Huang Hui Xiao
Guangxi Climate Center Naming,530022,China Shanghai Typhoon Institute Shanghai,200030,China Guangxi Meteorological Observatory Naming,530022,China College of Mathematical Sciences Guangxi Normal University Guilin,541004,China
国际会议
黄山
英文
461-465
2010-05-28（万方平台首次上网日期，不代表论文的发表时间）