• 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