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Mathematical Problems in Engineering
Volume 2012, Article ID 376010, 13 pages
Research Article

New Optimal Weight Combination Model for Forecasting Precipitation

1School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China
2The Key Laboratory of Water and Sediment Sciences, State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China

Received 9 February 2012; Accepted 21 March 2012

Academic Editor: Ming Li

Copyright © 2012 Song-shan Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to overcome the inaccuracy of the forecast of a single model, a new optimal weight combination model is established to increase accuracies in precipitation forecasting, in which three forecast submodels based on rank set pair analysis (R-SPA) model, radical basis function (RBF) model and autoregressive model (AR) and one weight optimization model based on improved real-code genetic algorithm (IRGA) are introduced. The new model for forecasting precipitation time series is tested using the annual precipitation data of Beijing, China, from 1978 to 2008. Results indicate the optimal weights were obtained by using genetic algorithm in the new optimal weight combination model. Compared with the results of R-SPA, RBF, and AR models, the new model can improve the forecast accuracy of precipitation in terms of the error sum of squares. The amount of improved precision is 22.6%, 47.4%, 40.6%, respectively. This new forecast method is an extension to the combination prediction method.