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An objective prediction model for typhoon rainstorm using particle swarm optimization: neural network ensemble

Author

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  • Hua-sheng Zhao
  • Long Jin
  • Ying Huang
  • Jian Jin

Abstract

A nonlinear ensemble prediction model for typhoon rainstorm has been developed based on particle swarm optimization-neural network (PSO-NN). In this model, PSO algorithm is employed for optimizing the network structure and initial weight of the NN with creating multiple ensemble members. The model input of the ensemble member is the high correlated grid point factors selected from the rainfall forecast field of Japan Meteorological Agency numerical prediction products using the stepwise regression method, and the model output is the future 24 h rainfall forecast of the 89 stations. Results show that the objective prediction model is more accurate than the numerical prediction model which is directly interpolated into the stations, so it can better been implemented for the interpretation and application of numerical prediction products, indicating a potentially better operational weather prediction. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Hua-sheng Zhao & Long Jin & Ying Huang & Jian Jin, 2014. "An objective prediction model for typhoon rainstorm using particle swarm optimization: neural network ensemble," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 427-437, September.
    • Handle: RePEc:spr:nathaz:v:73:y:2014:i:2:p:427-437
    DOI: 10.1007/s11069-014-1089-4
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    Cited by:

    1. Ying Huang & Long Jin & Hua-sheng Zhao & Xiao-yan Huang, 2018. "Fuzzy neural network and LLE Algorithm for forecasting precipitation in tropical cyclones: comparisons with interpolation method by ECMWF and stepwise regression method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 201-220, March.

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