Intelligent Analysis Method For Meteorological Data

Rainfall prediction is a very complicated and essential issue. The development of one thing from the past to the present and then develop into the future, there are always some internal rules in its development, some domestic and foreign experts and scholars explore and master the natural order of things actively and based on the scientific, the prediction guide things develop towards the expected direction rapidly. So accurate forecasting is the premise of correct decision-making. With the development of science and technology, almost all areas of human activity are able to provide a large number of statistical data. Therefore, establish a scientific prediction on the basis of statistical data.BP neural network has a slow convergence and is easy to fall into local minimum of the through the artificial neural network learning. Considering the global and fast characteristic in the Particle Swarm Optimization(PSO) and Gravitational Search Algorithm(GSA), the PSO-BPNN and GSA-BPNN precipitation prediction methods are proposed which optimize the BPNN initial weights and thresholds using the PSO and GSA respectively. We predict the experiment using the precipitation data of the A, B, C and D weather stations. The results show that the two methods have faster convergence speed and smaller prediction errors than the conventional BP neurnal network and the GSA-BPNN has higher accuracy than the PSO-BPNN.Based on the precipitation data for A, B, C and D of four weather stations, and apply to predictionand analysis of states of drought and water-logging in this region, in which the mean and standard deviation of information series are taken as the classification standard of precipitation states, and the past 52 years are classified into 5 classes according to the precipitation data, i.e.water-logging year, weak water-logging year, normal year, weak drought year and drought year. It is concluded that the result of prediction by the present method agrees with the reality.