Predictive modelling

Air pollution

Backpropagation

Ecology

Deep neural networks

Biology

Deep belief network

Pollution

Computer science

Haze

Organic chemistry

Geography

Environmental science

Artificial intelligence

Chemistry

Deep learning

Meteorology

Artificial neural network

Machine learning

Urban Climate

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, O3, 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.

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