ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Application of KNN Data Mining Algorithm to Haze Grade Forecasting in Beijing
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Abstract:
Based on meteorological data and PM2.5 concentration data of 2013， the statistical analysis shows that temperature, pressure, relative humidity, dew point temperature, surface wind, U wind， V wind and PM2.5 hourly concentration are the 7 key factors affecting the haze grade of Beijing Area. The attribute vectors of KNN training sample set are the above 7 factors and the label vector is haze grade. The classifier can be established combining KNN Data Mining Algorithm and its best value is parameter K=3. The classification accuracy of the 13 meteorological stations is 88.2%. The forecasting model based on KNN Algorithm has good accuracy. When it predicts no haze, the accuracy rate is 91.8% and missing forecast chance is very small. When mild, moderate or severe haze is predicted, the probability of empty forecast is only 4.7%, 1.4% and 2.6%, respectively. There was one frog haze weather process from 29 August to 2 September 2014 in Beijing Area. Its prediction results show the forecast accuracy of Nanjiao, Miyun and Yanqing is 74%, 64% and 84% respectively, but the accuracy of the haze grade remains to be further improved.
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