A Novel Hybrid Model Based on Spatiotemporal Correlation for Air Quality Prediction

Abstract

With the rapid development of urbanization, environmental pollution has drawn worldwide attention. Accurate air quality prediction is very significant for alleviating severe pollution conditions and human healthy life. A novel hybrid model that combines a spatiotemporal correlation analysis method and an effectively simple network is proposed to forecast the concentrations of air pollutants. Firstly, a new calculation method considering distance and PM 2.5 concentrations among stations is proposed to select stations strongly correlated with the target station on the basis of grey relation analysis. Secondly, a fully convolutional network is proposed to extract spatiotemporal features efficiently based on a temporal convolutional network. In addition, meteorological factors and other pollutants are selected as auxiliary factors to further improve the prediction accuracy. To verify the validity of the proposed model, the air quality and meteorological data collected from 40 monitoring stations in Fushun, which is an important industrial city in China, are applied for air quality prediction. The performance of models is evaluated by a series of metrics. The RMSE, MAE, and R 2 values of the proposed model are 12.505, 8.214, and 0.884 for forecasting the next hour PM 2.5 concentration. Compared with other conventional models and hybrid models, the proposed model verified that it outperforms the other models with air quality prediction.

Deep-learning architecture for PM2.5 concentration prediction: A review

“…[ 22 ] 2022 Beijing/Tianjin, China TL-DSTP-DANN H/S/T+3 15.97 11.75 20.00 - Dun et al. [ 35 ] 2022 Fushun, China DGRA-STCN H/S/T+2 12.50 8.21 88.40 - Ouyang et al. [ 29 ] 2022 Beijing, China/London, UK DC-STDGN H/M/T+(1-3) 30.58/13.42/4.28 29.63/12.15/3.03 - - Ejurothu [ 31 ].…”

“…, and Dun et al. [ 35 ] all incorporated spatial and temporal information to accurately predict PM 2.5 concentrations. Zhang et al.…”

“…Dun et al. [ 35 ] and Wang et al. [ 36 ] captured the spatial and temporal features via CNN or DNN architecture.…”

Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction

“…Dun et al [29] combined a spatio-temporal correlation and fully connected CNN model for prediction of air quality. Grey relation analysis, PM 2.5 concentrations, and a new calculation method for measuring distance were used during the process.…”