Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms

• Published in: Neural computing & applications 2021-11, Vol.33 (22), p.15073-15089
• Main Authors: Yeo, Inchoon, Choi, Yunsoo, Lops, Yannic, Sayeed, Alqamah
• Format: Article
• Language: English
• Subjects: Air pollution; Air quality; Algorithms; Artificial Intelligence; Artificial neural networks; Automatic weather stations; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Correlation coefficients; Data Mining and Knowledge Discovery; Deep learning; Image Processing and Computer Vision; Machine learning; Outdoor air quality; Polygons; Probability and Statistics in Computer Science; Railway stations; Review Article
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06082-8

This paper proposes a deep learning model that integrates a convolutional neural network and a gated recurrent unit with groups of neighboring stations to accurately predict PM 2.5 concentrations at 25 stations in Seoul, South Korea. The deep learning model uses observations obtained from one Korea Meteorological Administration (KMA) station, 25 National Institute of Environmental Research (NIER) stations, and 28 automatic weather stations (AWSs) throughout Seoul. To train the deep learning model, we use all available meteorological and air quality data observed between 2015 and 2017. With the trained model, we predict PM 2.5 concentrations at all 25 NIER stations in Seoul for 2018. This study also proposes a geographical polygon group model that determines the optimal number of neighboring NIER stations required to increase the accuracy of PM 2.5 concentration predictions at the target station. Comparing the model measures for each of the 25 monitoring sites in 2018, we find that the geographical polygon group model achieves an index of agreement of 0.82–0.89 and a Pearson correlation coefficient of 0.70–0.83. Compared to the method using only meteorological and air quality data from one target station (average IOA = 0.77) to predict PM 2.5 concentrations at the 25 stations in Seoul, the proposed method using geographical correlation-based neighboring NIER stations as polygonal groups (average IOA = 0.85) improves the PM 2.5 prediction accuracy by an average of about 10%. This approach, based on deep learning, can be updated to predict air pollution or air quality indices up to several days in advance.