LAUSR

At present, the numerical prediction models fail to predict effectively due to the lack of basic data of pollutant concentration in a short term in China. Therefore, it is necessary to study the statistical prediction methods based on historical data. The traditional Back Propagation Neural Network (BPNN) has been used to predict the pollutant concentration. The missing data also has an impact on modeling, and how to use historical data effectively of multiple monitoring stations in a city should be concerned. In this study, the Improved Newton Interpolation (INI) algorithm has been adopted to solve the problem of missing data, and assigning weight (AW) method has been proposed to enrich data of per station. The Neighbor-Principal Component Analysis (Neighbor-PCA) algorithm has been employed to reduce the dimension of data in order to avoid overfitting caused by high dimension and linear correlation of multiple factors. The strategy of early stopping and gradient descent algorithm have been utilized to avoid the slow convergence speed and overfitting by the traditional BPNN. The methods (INI, AW, Neighbor-PCA) have been integrated as a prediction model named NNP-BPNN. Forecasting experiments of PM$$_{2.5}$$2.5 have shown that the NNP-BPNN model can improve the accuracy and generalization ability of the traditional BPNN model. Specifically, the average root mean square error (RMSE) has been reduced by 24% and the average correlation relevancy has been increased by 9.4%. It took 20 s to implement BPNN model, it took 170 s to implement NN-BPNN model and it took 47 s to implement NNP-BPNN model. The time used by NNP-BPNN model is reduced by 72% than that of NN-BPNN model.