Comparison and Analysis of PM2.5 Forecast in Key Areas Based on the Neural Network Model and Numerical Model

Abstract

The PM2.5 forecast models of 95 cities in Beijing-Tianjin-Hebei and its surrounding cities (BTH); the Fenwei Plain (FWP); the border area of Jiangsu, Anhui, Shandong, and Henan (JASH); and the Yangtze River Delta (YRD) regions were established using BP neural network models, and the forecast was carried out for the next seven days in the autumn and winter in 2020. By comparing the forecast results of the BP neural network models, numerical model, and artificial correction, the PM2.5 forecast effects of the three methods were analyzed and evaluated. The results showed:① The performance of the short-term forecast based on the BP neural network was relatively good but was reduced in the medium and long term and systematically overestimated in four regions. The numerical model effects were lower than those of the BP neural network models. ② The accuracy rates of the PM2.5 forecast concentration by the three methods were generally low in the four regions, with an average of less than 50%, and the accuracy values in order from high to low were the BP neural network models, artificial correction, and the numerical model. The accuracy rates of IAQI levels of PM2.5 were significantly improved by the three methods, and the averages were above 65% in the first four days. The effects of the BP neural network models and artificial correction were similar, which were generally higher than those of the numerical model. ③ The numerical model had good effects in the BTH, JASH, and YRD regions, whereas it was the worst when forecasting moderately and above-polluted days in the FWP region. The BP neural network model had a good performance when forecasting short-term PM2.5 in the BTH, JASH, and FWP regions, whereas it was poor in the YRD region. In general, the performance of artificial correction was relatively good when forecasting moderate-level days and was close to the BP neural network model when forecasting heavily polluted days.