The Study Of Prediction Model On Mobile Communication Traffic And Lake Contour Evolution

A lot of research work on forecast have been done by many scholars. But in practical application, the prediction model is usually targeted at specific things. The precision of prediction results is also need to be improved. And the study on image predicting in the plane is not yet mature. The forecast analysis and modeling study are the main work in this study. The research is about the application of mobile traffic and lakes contour evolution, and the study is completed on the basis of the results of other researchers, the specific work is as follows:Support vector machine model is constructed firstly. The accuracy of the final result is determined by the training parameters, the ant colony algorithm is selected to optimize support vector machine. Through experiment simulation, the optimized model has obvious advantages on prediction accuracy and timeliness.To improve the prediction accuracy of busy telephone traffic, this paper proposes a combined forecasting model which takes the influence of multiple factors into consideration and combines Empirical Mode Decomposition and Gaussian process model and gray prediction model. the key factors which influence the busy telephone traffic are obtained firstly. Then Empirical Mode Decomposition is used to decompose the traffic data to get the components with different frequency. The IMF component are loaded into Gaussian process model to predict, while the trend component is loaded into gray prediction model to predict, finally the forecasting result is achieved by the superposition of each predictive values. The simulation results show that the proposed model has the superiority of higher prediction accuracy and easier to implement.In order to research the application of forecasting model more extensively, the prediction model applied to the image is proposed. Lake of Zhangjiangtoumucuo is selected as the research object, the boundary information of the lake is extracted from its remote sensing image in different periods, and the outline of the lake is generated. Choose an origin in the middle of the study area, and draw the rays, and get the original data for predicting. The data is loaded into gray prediction model to predict and get the forecasting image of the lake, then validate the prediction results, the final results show that the research method of this article in predicting the lake outline change has certain rationality and validity.