Smog prediction based on the deep belief - BP neural network model (DBN-BP)

Smog pollution is becoming a significant problem for people worldwide, becoming an essential threat to the global environment. Many studies on haze already exist, which still need to continue in-depth research to better deal with haze problems. Due to its unique geographical environment, Sichuan has become one of the areas with severe smog pollution. Therefore, the research and prediction of smog pollution in Sichuan has become an urgent need. This paper proposes a deep learning technology based on a Deep Belief-Back Propagation neural network. It makes in-depth prediction research by using the air pollution data of PM2.5, PM10, O-3, CO NO2, and SO2 in Sichuan smog to provide a decision-making basis for predicting and preventing smog polluted weather. According to the prediction results of the model, the concentrations of PM2.5 and PM10 in Chengdu were predicted. The analysis shows that the larger the number of hidden layers in the belief network, the higher the prediction accuracy. Under the same network, the prediction accuracy of PM2.5 is significantly higher than that of PM10. Compared with the traditional Back Propagation neural network, the prediction effect of the Deep Belief-Back Propagation neural network is better.

Automated summary

Smog pollution is a significant global problem requiring further research and prediction. This paper proposes a deep learning technology based on a Deep Belief-Back Propagation neural network to predict and prevent smog pollution using air pollution data in Sichuan. The results show that the prediction accuracy is higher with more hidden layers, and PM2.5 can be predicted more accurately than PM10 using this network.