PM_2.5 prediction model based on weighted KNN-BP neural network

Through the weighted KNN-BP neural network method determined by membership function , the dynamic real-time prediction model of PM_2.5 concentration was established. The concentration of six pollutants, i.e. PM_2.5, PM₁₀, NO₂, CO, O₃ and SO₂, six meteorological data including weather condition, temperature, pressure, humidity, wind speed and wind direction in the first hour, as well as the days of a week and the hours of the days for projection were regarded as the dimensions of the KNN instance. Three nearest neighbors were selected and, according to the Euclidean distance obtained, the membership weight of each neighbor point variable determined. Finally, the dimension of all nearest neighbor points were taken as the input layer of BP neural network, and the next hour PM_2.5 concentration to be predicted as the output layer data. The method avoided the problem that the traditional BP neural network method failed to reflect the influence of the data in the historical window on the current predicting. The data of 2014-05-01 from 00:00 to 23:00 2014-09-10 in Dongcheng District monitoring station in Beijing was tested. The results showed that the prediction model with weighted KNN-BP neural network had the lowest deviation compared with other methods, and the stability showed the best. Therefore, this model is an effective method for the PM_2.5 real time prediction.