Deep Spatio-temporal Learning Model for Air Quality Forecasting
Keywords:spatio-temporal data mining, global prediction, 3D convolution, dynamic neural advection
In recent years, air pollution has seriously affected people’s production and life, so the air prediction has become a research hotspot in recent years. When analyzing air data, it is found that this type of data has not only temporal correlation, but also spatial correlation. For these temporal and spatial characteristics, this paper studies deep spatio-temporal learning method to global prediction. The purpose is to learn the evolution rule behind the spatio-temporal sequence, and give an estimation for future state. To be specific, we propose two novel forecasting models based on video processing technology: Spatio-temporal Orthogonal Cube model (STOR-cube) and Spatio-temporal Dynamic Advection model (ST-DA), which effectively capture the spatio-temporal correlation and accurately predict the long-term air quality. STOR-cube contains three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motion, and an output branch for coupling the first two mutually orthogonal branches to generate a prediction frame. ST-DA constructs a spatio-temporal reasoning network to learn the characteristics of the spatio-temporal domain, and its impact on the future is explicitly modeled by pixel motion. Experiments results on the real-world datasets demonstrate our proposed approach significantly outperforms the state-of-the-art ones. Moreover, our model can be extended to other spatio-temporal data prediction tasks.
 Finn, C. ; Goodfellow, I. ; Levine, S. (2016). Unsupervised learning for physical interaction through video prediction, Proceedings of the 30th International Conference on Neural Information Processing Systems, 64-72, 2016.
 Fan, H. ; Zhu, L. ; Yang, Y. (2019) Cubic LSTMs for Video Prediction, 33rd AAAI Conference on Artificial Intelligence, 33(01), 8263-8270, 2019. https://doi.org/10.1609/aaai.v33i01.33018263
 He, Q. ; Huang, B. (2018). Satellite-based mapping of daily high-resolution ground PM2. 5 in China via space-time regression modeling, Remote Sensing of Environment, 206, 72-83, 2018. https://doi.org/10.1016/j.rse.2017.12.018
 Hillmer, S. C. ; Tiao, G. C. (2012). An ARIMA-Model-Based Approach to Seasonal Adjustment, Journal of the American Statistical Association, 377(77), 63-70, 2012. https://doi.org/10.1080/01621459.1982.10477767
 Huang, B. ; Wu, B. ; Barry, M. (2010). Geographically and Temporally Weighted Regression for Modeling Spatio-temporal Variation in House Prices, International Journal of Geographical Information Science, 24(3), 383-401, 2010. https://doi.org/10.1080/13658810802672469
 Ji, S. ; Xu, W. ; Yang, M. ; Yu, K. (2012). 3D convolutional neural networks for human action recognition, IEEE transactions on pattern analysis and machine intelligence, 35(1), 221-231, 2012. https://doi.org/10.1109/TPAMI.2012.59
 Li, J ; Pan, S.X. ; Huang, L. ; Zhu, X. (2019). A Machine Learning Based Method for Customer Behavior Prediction, Tehnicki vjesnik-Technical Gazette, 26(6), 1670-1676, 2019. https://doi.org/10.17559/TV-20190603165825
 Moreira-Matias, L.; Gama, J.; Ferreira, M.; Mendes-Moreira, J.; Damas, L. (2013). Predicting Taxi-passenger demand using streaming data, IEEE Transactions on Intelligent Transportation Systems, 14(3), 1393-1402, 2013. https://doi.org/10.1109/TITS.2013.2262376
 Pirbadali-Somarin, A. ; Peyghambarzadeh, S. (2020). Air pollution by heavy metals from petrochemical incinerators: measurement and dispersion modelling, Environmental Engineering and Management Journal, 19, 379-390, 2020. https://doi.org/10.30638/eemj.2020.036
 Prasad, K.; Gorai, A. K.; Goyal, P. (2016). Corrigendum to í—Development of ANFIS Model for Air Quality Forecasting and Input Optimization for Reducing the Computational Cost and Timeí—, Atmospheric Environment, 246-262, 2016. https://doi.org/10.1016/j.atmosenv.2016.01.007
 Qin, L. L.; Yu, N. W.; Zhao, D. H. (2018). Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video, Tehnicki vjesnik-Technical Gazette, 25(1), 528-535, 2018. https://doi.org/10.17559/TV-20171229024444
 Qiu, Z.; Yao, T. ; Mei, T. (2017). Learning spatio-temporal representation with pseudo-3d residual networks, Proceedings of IEEE International Conference on Computer Vision (CVPR), 5533- 5541, 2020. https://doi.org/10.1109/ICCV.2017.590
 Saide, P. E.; Mena-Carrasco, M.; Tolvett, S.; Hernandez, P.; Carmichael, G. R. (2016). Air quality forecasting for Winter-time PM2. 5 episodes occurring in multiple cities in central and southern Chile, Journal of Geophysical Research: Atmospheres, 121(1), 558-575, 2016. https://doi.org/10.1002/2015JD023949
 Shi, X.; Chen, Z.; Wang, H.; Yeung, D. Y.; Wong, W. K.; Woo, W. C.(2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting, Advances in neural information processing systems, 28, 802-810, 2015.
 Simo, A.; Dzitac, S.; Frigura-Iliasa, F. M.; Musuroi, S.; Andea, P.; Meianu, D. (2020). Technical Solution for a Real-Time Air Quality Monitoring System, Internation journal of computers computers communications & control, 15(4), 2020. https://doi.org/10.15837/ijccc.2020.4.3891
 Srivastava, N.; Mansimov, E.; Salakhudinov, R. (2015). Unsupervised learning of video representations using lstms, International conference on machine learning, 843-852, 2015.
 Tran, D.; Bourdev, L.; Fergus, R.; Torresani, L.; Paluri, M. (2015). Learning spatiotemporal features with 3d convolutional networks, In Proceedings of the IEEE international conference on computer vision, 4489-4497, 2015. https://doi.org/10.1109/ICCV.2015.510
 Wang, L.; Hao, Z.; Han, X.; Zhou, R. (2018). Gravity Theory-Based Affinity Propagation Clustering Algorithm and Its Applications, Tehnicki vjesnik, 25(4), 1125-1135, 2018. https://doi.org/10.17559/TV-20180504150204
 Wang, S.; Li, Y.; Zhang, J.; Meng, Q.; Meng, L.; Gao, F. (2020) . PM2. 5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2. 5 Forecasting, 26th International Conference on Knowledge Discovery and Data Mining, 2020. https://doi.org/10.1145/3397536.3422208
 Wu, H.; Tsai, A.; Wu, H. (2019) . A hybrid multi-criteria decision analysis approach for environmental performance evaluation: an example of the TFT-LCD manufacturers in Taiwan, Environmental Engineering and Management Journal, 18, 597-616, 2019. https://doi.org/10.30638/eemj.2019.056
 Xie, S.; Sun, C.; Huang, J.; Tu, Z.; Murphy, K. (2018) . Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification, In Proceedings of the European Conference on Computer Vision (ECCV), 305-321, 2018. https://doi.org/10.1007/978-3-030-01267-0_19
 Yang, W.; Deng, M.; Xu, F.; Wang, H. (2018) . PM2. 5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2. 5 Forecasting, Atmospheric Environment, 181, 12-19, 2018.
 Yi, X.; Zhang, J.; Wang, Z.; Li, T.; Zheng, Y. (2018) . Deep distributed fusion network for air quality prediction, In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 965-973, 2018. https://doi.org/10.1145/3219819.3219822
 Zhang, L.; Li, D.; Guo, Q. (2020). Deep Learning from Spatio-temporal Data using Orthogonal Regularizaion Residual CNN for Air Prediction, IEEE Access, 8, 66037-66047, 2020. https://doi.org/10.1109/ACCESS.2020.2985657
 Zhang, J.; Zheng, Y.; Qi, D.; Li, R.; Yi, X.; Li, T. (2018). Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks, Artificial Intelligence, 259, 147-166, 2018. https://doi.org/10.1016/j.artint.2018.03.002
 Zheng, Y.; Capra, L.; Wolfson, O.; Yang, H. (2014) . Urban computing: concepts, methodologies, and applications, ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), 1-55, 2014. https://doi.org/10.1145/2629592
 Zong, M.; Wang, R.; Chen, Z.; Wang, M.; Wang, X.; Potgieter, J. (2020) . Multi-cue-based 3D residual network for action recognition, Neural Computing and Applications, 1-15, 2020. https://doi.org/10.1007/s00521-020-05313-8
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.