An Air Quality Prediction Model Based on a Noise Reduction Self-Coding Deep Network

• Published in: Mathematical problems in engineering Vol. 2020; no. 2020; pp. 1 - 12
• Main Authors: Gao, Zhitao, Hong, Li, Dai, Xun, Cai, Jianxian, Qiu, Zhongchao
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; Hindawi Limited
• Subjects: Accuracy; Advantages; Air monitoring; Air pollution; Air quality; Algorithms; Artificial intelligence; Big Data; Classification; Decision making; Deep learning; Emergency communications systems; Environmental monitoring; Feature extraction; Machine learning; Mathematical problems; Model accuracy; Neural networks; Noise monitoring; Noise prediction; Noise reduction; Outdoor air quality; Pollutants; Prediction models; Prevention; Principal components analysis; Regression analysis; Time series; Weather forecasting; China
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2020/3507197

Aiming at remedying the problem of low prediction accuracy of existing air pollutant prediction models, a denoising autoencoder deep network (DAEDN) model that is based on long short-term memory (LSTM) networks was designed. This model created a noise reduction autoencoder with an LSTM network to extract the inherent air quality characteristics of original monitoring data and to implement noise reduction processing on monitoring data to improve the accuracy of air quality predictions. The LSTM network structure in the DAEDN model was designed as bidirectional LSTM (Bi-LSTM) to solve the problem of a lag in the unidirectional LSTM prediction results and thereby to further improve the prediction accuracy of the prediction model. Using air pollutant time series data, the DAEDN model was trained using hourly PM2.5 concentration data collected in Beijing over 5 years. The experimental results show that the DAEDN model can extract more stable features from the noisy input after training was completed. The models were evaluated using RMSE and MAE, and the results show that the indexes are 15.504 and 6.789; compared with unidirectional LSTM, it is reduced by 7.33% and 5.87%, respectively. In addition, the new prediction model essentially considered the time series properties of the prediction of the concentration of spatial pollutants and the fully integrated environmental big data, such as air quality monitoring, meteorological monitoring, and forecasting.