A study of precipitation forecasting for the pre-summer rainy season in south china based on a back-propagation neural network

HIGHLIGHTS

• What: Three schemes are applied to improve the model performance: predictors are selected based on individual meteorological stations within the region rather than the region as a whole; the triangular irregular network (TIN) is proposed to preprocess the observed precipitation data for input of the BPNN model while simulated/forecast precipitation is the expected output; and_(3) a genetic algorithm is used for the hyperparameter optimization of the BPNN. This study shows the feasibility that BPNN can predict not only the amount of precipitation but also the spatial distribution skillfully. Aiming at improving the simulation of the . . .
  

   

  

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