A new integrating forecast and its application in short-term climate prediction〖JP〗
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Abstract:
The existing problems of the traditional weight integrating forecast methods and the application in climate prediction are analyzed. A new method based on data mining is presented, which uses BP artificial neural network to build the integrating forecast classifier to integrate the forecast results of sub-methods. According to the features of different forecast objects, this method can change weight dynamically, which overcomes the shortage of the traditional weight integrating forecasts that cannot change weight after been decided and overcomes the shortage that cannot get the optimal results. By predicting the precipitation and average temperature of Chengkou County in January, and spring drought index of Chongqing from 2001 to 2007, the experiment results show that the reliability and accuracy of the proposed model are better than those of the sub-methods and other integrating forecast methods, which proves the effectiveness of this method.
