The Study of Precipitation Forecast Model on EMD-RBF Neural Network - A Case Study on Northeast China

Abstract:

Based on the principal of forecast of Artificial Neural Network, Radial Basis Function neural network and Radial Basis Function neural network based on EMD were introduced into the field of precipitation forecasting in this article. With the precipitation data of 27 sites from1950-2010, EMD-RBF network was set up, and the difference between the predictive value and the actual precipitation data was discussed. The results showed that the correlation Of EMD-RBF forecast precipitation and actual precipitation is more than 0.9. Of all sites, the maximum relative prediction error of 17 sites is less than 10%, the maximum relative error does not exceed 15%.The EMD-RBF model had good quality on forecasting precision, which provided a new method for precipitation forecasting.

• Periodical: Applied Mechanics and Materials (Volumes 641-642)
• Pages: 119-122
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.641-642.119
• Online since: September 2014
• Authors: Xiao Sun*, Shi Fan Qiao, Ji Ren Xie
• Keywords: Empirical Mode Decomposition (EMD), Forecasts, Precipitation, Radial Basis Function

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DOI: 10.1098/rspa.1998.0193