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A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury

Author

Listed:
  • Hong Lu

    (Climate Center of Guangxi Zhuang Autonomous Region)

  • Yi Ou

    (Climate Center of Guangxi Zhuang Autonomous Region)

  • Chuan Qin

    (Climate Center of Guangxi Zhuang Autonomous Region)

  • Long Jin

    (Climate Center of Guangxi Zhuang Autonomous Region)

Abstract

On the basis of the daily temperature and precipitation data of Guangxi and the NCEP/NCAR reanalysis data and forecast field data, the paper aims to determine the significant nonlinearity and temporal variability of the forecast quantity series and the overfitting that can easily appear in the forecast modeling of a single fuzzy neural network model and many adjustable parameters that are difficult to determine objectively. Thus, an ensemble forecasting model of fuzzy neural network bagging for 72-h forecast of low-temperature chilling injury is developed. The forecast results of independent samples show that under the same forecast modeling sample (N = 299) and forecasting factor (M = 9), the fuzzy neural network bagging ensemble forecasting model obtains a mean absolute error of 13.91. By contrast, the mean absolute errors of the single fuzzy neural network forecasting model and the linear regression forecast are 15.82 and 18.13, respectively. The fuzzy neural network bagging ensemble forecast error is lower by 12.07 and 23.27%, respectively, compared with the latter two methods, showing a better forecasting skill. This improved performance is mainly due to the ensemble individuals of the fuzzy neural network bagging ensemble forecasting model with playback sampling. Different ensemble individuals are obtained. The ensemble enhances the generalization performance and forecast stability of the fuzzy neural network bagging ensemble forecasting model. Thus, this model has better applicability in forecasting nonlinear low-temperature chilling injury.

Suggested Citation

  • Hong Lu & Yi Ou & Chuan Qin & Long Jin, 2021. "A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury," 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. 105(2), pages 2147-2160, January.
    • Handle: RePEc:spr:nathaz:v:105:y:2021:i:2:d:10.1007_s11069-020-04393-y
    DOI: 10.1007/s11069-020-04393-y
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    References listed on IDEAS

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    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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