The Application To Short-Term Climate Prediction Based On Genetic Algorithms And BP Neural Networks

Short-term Weather Prediction, in comparison with Long-term Weather Prediction, is the forecast of climatic change in a relatively short period of time. This article tries to establish a Short-term Weather Prediction model based on GA-BP Algorithms, which is a combination of Artificial Neural Network (ANN) and Genetic Algorithms (GA). Using this new method can provide a more effective guarantee for national economic and social development by means of improving the accuracy of traditional method of statistic and experience.The framework and research are listed as follows:1.In this paper, introduced artificial neural networks in short-term climate prediction in the use of the status quo, and explore the prospects for its use. The paper clarifies the basic principle and the study process of BP neural network and Genetic Algorithms,analyses the existing weakness and introduces some general improvement measure.2. Making use of excellent global searching ability of GA and fine learning ability of ANN, the article designs GA-BP Algorithms by using GA to optimize initial weights of neural network. The GA-BP Algorithms, to some extent, can endure the local minima problem widely existed in neural network model training. Then,this method and the characteristics of short-term climate prediction to combine the establishment of the forecast model.3. Take the climatic change data during the recent 46 years in Yanting County, Sichuan Province as an example, we conduct inspection and examination on the weather prediction based on GA-BP Algorithms, apply MATLAB software to simulation and compare it with the actual data. The result indicates this algorithm has better stability and precision. This method can provide a reference for short-term climate prediction in small-scale region.