A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction

Neurocomputing

Volumn 314, Issue , 2018, Pages 198-206

  Wang, Junshan ^a   Song, Guojie ^a  

^a Peking University   (China)

Author keywords

Air quality prediction; Deep learning; Spatial temporal data

Indexed keywords

DEEP LEARNING; ELECTRIC SUBSTATIONS; FORECASTING; LONG SHORT-TERM MEMORY; METEOROLOGY;

AIR QUALITY PREDICTION; DATA-DRIVEN APPROACH; METEOROLOGICAL DATA; MONITORING STATIONS; PARTITIONING STRATEGIES; PREDICTION ACCURACY; SPATIAL CORRELATIONS; SPATIAL-TEMPORAL DATA;

AIR QUALITY;

ACCURACY; AIR MONITORING; AIR QUALITY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHINA; DEEP SPATIAL TEMPORAL ENSEMBLE MODEL; METEOROLOGICAL PHENOMENA; PREDICTION; PRIORITY JOURNAL; STATISTICAL MODEL; WEATHER;

EID: 85049832090     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2018.06.049     Document Type: Article

References (22)

1. Grover, A., Kapoor, A., Horvitz, E., A deep hybrid model for weather forecasting. Proceedings of the twenty-First ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, ACM, 379–386.
2. McCollister, G.M., Wilson, K.R., Linear stochastic models for forecasting daily maxima and hourly concentrations of air pollutants. Atmos. Environ. (1967) 9:4 (1975), 417–423.
3. Wayland, R., White, J., Dickerson, P., Dye, T., Communicating real-time and forecasted air quality to the public. EM-Pittsburgh-air and waste management association, 2002, 28–36.
4. Vardoulakis, S., Fisher, B.E., Pericleous, K., Gonzalez-Flesca, N., Modelling air quality in street canyons: a review. Atmosp. Environ. 37:2 (2003), 155–182.
5. Pfender, W., Graw, R., Bradley, W., Carney, M., Maxwell, L., Use of a complex air pollution model to estimate dispersal and deposition of grass stem rust urediniospores at landscape scale. Agric. For. Meteorol. 139:1 (2006), 138–153.
6. Stadlober, E., Hörmann, S., Pfeiler, B., Quality and performance of a pm10 daily forecasting model. Atmos. Environ. 42:6 (2008), 1098–1109.
7. Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., Brasseur, O., A neural network forecast for daily average pm 10 concentrations in Belgium. Atmos. Environ. 39:18 (2005), 3279–3289.
8. Jiang, Z., Mao, B., Meng, X., Du, X., Liu, S., Li, S., An air quality forecast model based on the BP neural network of the samples self-organization clustering. Proceedings of the Sixth International Conference on Natural Computation (ICNC), vol. 3, 2010, IEEE, 1523–1527.
9. Granger, C.W., Investigating causal relations by econometric models and cross-spectral methods. Econom. J. Econom. Soc., 1969, 424–438.
10. Zhu, J.Y., Sun, C., Li, V.O., Granger-causality-based air quality estimation with spatio-temporal (st) heterogeneous big data. Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2015, IEEE, 612–617.
11. Hochreiter, S., Schmidhuber, J., Long short-term memory. Neural Comput. 9:8 (1997), 1735–1780.
12. W. Zhu, C. Lan, J. Xing, W. Zeng, Y. Li, L. Shen, X. Xie, Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks, Proceedings of the AAAI, 2016, 3697–3703.
13. Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., Baklanov, A., Real-time air quality forecasting, part i: History, techniques, and current status. Atmos. Environ. 60 (2012), 632–655.
14. McKeen, S., Grell, G., Peckham, S., Wilczak, J., Djalalova, I., Hsie, E.-Y., Frost, G., Peischl, J., Schwarz, J., Spackman, R., et al. An evaluation of real-time air quality forecasts and their urban emissions over eastern texas during the summer of 2006 second Texas air quality study field study. J. Geophys. Res. Atmos., 114(D7), 2009.
15. Donnelly, A., Misstear, B., Broderick, B., Real time air quality forecasting using integrated parametric and non-parametric regression techniques. Atmos. Environ. 103 (2015), 53–65.
16. Guizilini, V.C., Ramos, F.T., A nonparametric online model for air quality prediction. Proceedings of the AAAI, 2015, 651–657.
17. Nguyen-Tuong, D., Peters, J.R., Seeger, M., Local gaussian process regression for real time online model learning. Proceedings of the Advances in Neural Information Processing Systems, 2009, 1193–1200.
18. Burrows, W.R., Benjamin, M., Beauchamp, S., Lord, E.R., McCollor, D., Thomson, B., Cart decision-tree statistical analysis and prediction of summer season maximum surface ozone for the vancouver, montreal, and atlantic regions of canada. J. Appl. Meteorol. 34:8 (1995), 1848–1862.
19. Zheng, Y., Liu, F., Hsieh, H.-P., U-air: when urban air quality inference meets big data. Proceedings of the Nineteenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2013, ACM, 1436–1444.
20. Hsieh, H.-P., Lin, S.-D., Zheng, Y., Inferring air quality for station location recommendation based on urban big data. Proceedings of the Twenty-First ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, ACM, 437–446.
21. Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., Li, T., Forecasting fine-grained air quality based on big data. Proceedings of the Twenty-First ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, ACM, 2267–2276.
22. Bezdek, J.C., Ehrlich, R., Full, W., FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10:2–3 (1984), 191–203.