Application of BP neural network model based on Grain in Ear to medium and long term wet season rainfall forecasting
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Abstract:
In order to improve the accuracy of medium and long term wet season rainfall forecasting,a Hybrid models based on the Grain in Ear and BP neural network is established in this study,and applied to the forcasting of rainfall in mid and long term wet seasons in Beijing.The results show that the hybrid model can effectively improve the accuracy of the rainfall forecasting,in comparison with the traditional BP model.The correlation coefficient between the simulated and the measured rainfall is 0.78,which is much better than the traditional BP model of 0.42.The hybrid model also has a 40% improvement in terms of the forecasting pass rate over the traditional BP model.The Grain in Ear can fully explore the useful information hidden in the original data,reduce interfere of noise data (e.g.,extreme values),and effectively improve the forecasting accuracy.This study combines the traditional 24 solar terms with artificial intelligence forecasting technology and provides a new idea for medium and long term wet season rainfall forecasting.
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