Encoder-decoder model for forecast of PM2.5 concentration per hour

Proceedings - 2018 1st International Cognitive Cities Conference, IC3 2018

Volumn , Issue , 2018, Pages 45-50

  Yan, Leiming ^a   Wu, Yaowen ^a   Yan, Luqi ^a   Zhou, Min ^a  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

Author keywords

Air pollution; Encoder decoder model; Neural networks; PM2.5

Indexed keywords

AIR POLLUTION; AIR QUALITY; DECODING; MEAN SQUARE ERROR; METEOROLOGY; NEURAL NETWORKS; SIGNAL ENCODING;

AIR QUALITY INDICES; BP NEURAL NETWORKS; ENCODER-DECODER; METEOROLOGICAL DATA; METEOROLOGICAL FACTORS; PM2.5; PM2.5 CONCENTRATION; ROOT MEAN SQUARE ERRORS;

FORECASTING;

EID: 85060403221     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IC3.2018.00020     Document Type: Conference Paper

References (16)

1. F. Biancofiore, M. Busilacchio, M. Verdecchia, B. Tomassetti, E. Aruffo, S. Bianco, et al., "Recursive neural network model for analysis and forecast of PM10 and PM2.5," Atmospheric Pollution Research, 2017, 8(4):652-659.
2. X. Qiu, L. Duan, J. Gao, S. Wang, F. Chai, J. Hu, J. Zhang, Y. Yun, "Chemical composition and source apportionment of PM10 and PM2. 5 in different functional areas of Lanzhou, China," Journal of Environmental Sciences, 2016, 40(02):75-83.
3. X. Du, Q. Kong, W. Ge, S. Zhang, L. Fu, "Characterization of personal exposure concentration of fine particles for adults and children exposed to high ambient concentrations in Beijing, China," Journal of Environmental Sciences, 2010, 22(11):1757-1764.
4. S. Tanday, "India launches air quality index to tackle pollution problems," The Lancet Oncology, 2015,16(5).
5. W. Liu, G. Zheng, X. Tian, G. Lang, Q. Feng, "Multivariate Regression Analysis Model of Related Factors of PM2.5 in Xi'an City," Mathematics in Economics, 2015, 32(01):85-88.
6. M. Amodio, E. Andriani, G. De, M C Placentino. "An integrated approach to identify the origin of PM10 exceedances," Environmental Science & Pollution Research International, 2012, 19(8):3132-3141.
7. Q. Fu, "Research on Haze Forecasting Method Based on Multiple Linear Regressio," Computer Science, 2016, 43(S1):526-528.
8. X. Feng, Q. Li, Y. Zhu, J. Hou, L. Jin, J. Wang, "Artificial neural networks forecasting of PM 2.5 pollution using air mass trajectory based geographic model and wavelet transformation," Atmospheric Environment, 2015, 107:118-128.
9. R. Sivacoumar, A. D. Bhanarkar, S. K. Goyal, A. L. Aggarwal, "Air pollution modeling for an industrial complex and model performance evaluation," Environmental Pollution, 2001, 111(3):471-477.
10. Y. Hu, X. Duan, "Study on PM2.5 Diffusion and Prediction Based on Gaussian Plume and Multiple Linear Regression Model," Journal of Arid Land Resources and Environment, 2015, 29(6):86-92.
11. Y. Zhang, Y. He, J. Zhu, "Research on Forecasting Problem Based on Multiple Linear Regression Model PM2.5," Journal of Anhui Science and Technology University, 2016, 30(3):92-97.
12. K. R. Baker, K. M. Foley, "A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5," Atmospheric Environment, 2011, 45(22):3758-3767.
13. T. Ma, D. Liu, X. Li, C. Sun, "Prediction of PM2.5 Concentration in Fuzzy Neural Network Based on Improved PSO," Computer Engineering and Design, 2014,35(09):3258-3262.
14. P. Pérez, A. Trier, J. Reyes, "Prediction of PM 2.5, concentrations several hours in advance using neural networks in Santiago, Chile," Atmospheric Environment, 2000, 34(8):1189-1196.
15. K. Cho, B. V. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, et al, "Learning phrase representations using rnn encoderdecoder for statistical machine translation," Conference on Empirical Methods in Natural Language Processing(EMNL2014), October 25-29, 2014, Doha, Qatar.
16. I. Sutskever, O. Vinyals, Q. V. Le, "Sequence to Sequence Learning with Neural Networks," arXiv:1409.3215 [cs.CL], 2014