DocumentCode
3311280
Title
The Effect of Model Input on Forecast Results of the Neural Network Ensemble Model
Author
Jin, Long ; Huang, Ying ; Yu, Hui ; Huang, Xiaoyan ; Xiao, Hui
Author_Institution
Guangxi Climate Center, Nanning, China
Volume
2
fYear
2010
fDate
28-31 May 2010
Firstpage
461
Lastpage
465
Abstract
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.
Keywords
data mining; feature extraction; geophysics computing; neural nets; regression analysis; data mining technology; empirical orthogonal function; feature extraction method; neural network ensemble prediction model; stepwise regression method; typhoon intensity; Accuracy; Computer networks; Feature extraction; Genetics; Mathematical model; Meteorology; Neural networks; Predictive models; Typhoons; Weather forecasting; ensemble prediction; feature extraction; neural network; typhoon intensity;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Science and Optimization (CSO), 2010 Third International Joint Conference on
Conference_Location
Huangshan, Anhui
Print_ISBN
978-1-4244-6812-6
Electronic_ISBN
978-1-4244-6813-3
Type
conf
DOI
10.1109/CSO.2010.17
Filename
5532939
Link To Document