Research And Application Of SVM Classifier In The Local Area Meteorological Data

With the advancement of science and technology, the development of information, the Promotion of Meteorological Research Technology, and the growing in the field of meteorology data with each passing day so fast. It is an important task of the meteorological research section to find the valuable information from the massive meteorological data. Weather information is closely related with people’s lives. People’s social lives and production is directly influenced by the weather. If data mining can be applied to the meteorological data, It will make full use of the available information, Those information not only can improve the accuracy of weather forecasts and the ability of disaster weather warning, but also can guide the local industrial and agricultural production and raise the living standards of the people.In data mining, SAS、SPSS、MATLAB are usually used. Even some open software like WEKA、ARMiner. Different software is used in different model. Data mining are widely used in computer. It is widely used in invasion detection based on HTTP、TCP、UDP. Internet use data mining in analyzing users’ clicks. It is also used in consumer classification, set reasonable consumer meals, all of these bring huge benefit to company.Classification is a very important technology. It is widely used in weather report. Machine learning is widely used in data mining. This article used KNN and SVM classification and ensemble learning to construct ensemble classification then analyzed and studied the meteorological data of a local meteorological station. After modeling, we send the samples to the model, finally, we get the result, through a series of comprising, we show the result through viewable charts, in order to improve that our model is better in efficiency and accuracy rate.This paper has carried out the following research:Analyzing the present situation of meteorological data mining, Studying the ensemble classifier using in local meteorological data mining. We also construct and analyze single SVM classifier model; Construct and analyze SVM-based Bagging ensemble classifier model; Construct and analyze SVM-based AdaBoost ensemble classifier model. With the meteorological data of local area, these ensemble classifiers is applied to predict the raining. Performance and the accuracy of these three models are compared by using KNN algorithm.The results of this research provide a basis decision support making for the local weather bureau and also provide the guidance effect on the local residents of their social life and industrial production.