Deep neural network for pm2.5 pollution forecasting based on manifold learning

Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017

Volumn 2017-December, Issue , 2017, Pages 236-240

  Xie, Jingjing ^a  

^a CHONGQING TECHNOLOGY AND BUSINESS UNIVERSITY   (China)

Author keywords

Deep neural network; Forecasting; Manifold learning; PM2.5

Indexed keywords

FORECASTING; NEURAL NETWORKS; POLLUTION; SYSTEMS ENGINEERING;

DEEP BELIEF NETWORK (DBN); FORECASTING MODELING; MANIFOLD LEARNING; METEOROLOGICAL FACTORS; PM2.5; PM2.5 CONCENTRATION; POLLUTION CONCENTRATION; REGRESSION NEURAL NETWORKS;

DEEP NEURAL NETWORKS;

EID: 85047441990     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SDPC.2017.52     Document Type: Conference Paper

References (21)

1. J. Chen, J. Lu, J. C. Avise, "Seasonal modeling of PM2.5 in California's San Joaquin Valley, " Atmos. Environ., vol 92, pp. 182-190, August 2014.
2. I. Djalalova, L. D. Monache, J. Wilczak, "PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model, " Atmos. Environ., vol 108, pp. 76-87, May 2015.
3. P. Wang, H. Zhang, Z. D. Qin, G. S. Zhang, "A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting, " Atmos. Pollut. Res., doi.org/10.1016/j.apr.2017.01.003, in press.
4. L. Jian, Y. Zhao, Y. P. Zhu, M. Zhang, D. Bertolatti, "An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China, " Sci. Total Environ., vol 426, pp. 336-345, June 2012.
5. Y. Chen, R. Shi, S. Shu, W. Gao, "Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis, " Atmos. Environ., vol 74, pp. 346-359, August 2013.
6. R. Shad, S. M. Mohammad, A. Aliakbar, S. Arefeh, "Predictiing air pollution using fuzzy genetic linear membership kriging inGIS, " Comput. Environ. Urban Syst., vol 33, pp. 472-481, November 2009.
7. T. E. Alhanafy, F. Zaghlool, A. S. EI, D. Moustafa, "Neuro fuzzy modeling scheme for the prediction of air pollution, " J. Am. Sci., vol 6, pp. 605-616, December 2010.
8. M. Ashish, B. Rashmi, "Prediction of daily air pollution using wavelet decomposition and Adaptive-Network-based fuzzy inference system, " Int. J. Environ. Sci., vol 2, pp. 185-196, September 2011.
9. C. F. Morabito, M. Versaci, "Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data, " Neural Netw., vol 16, pp. 493-506, April-May 2003.
10. Y. Yildirim, M. Bayramoglu, "Adaptive neuro-fuzzy based modeling for predicition of air pollution daily levels in city of Zonguldak, " Chemosphere, vol 63, pp. 1575-1582, September 2006.
11. M. Oprea, S. F. Mihalache, M. Popescu, "A comparative study of computational intelligence techniques applied to PM2.5 air pollution forecasting, " International Conference on Computers Communications & Control pp. 103-108, June 2016.
12. M. F. Ababneh, A. O. AL-Manaseer, M. H. Btoush, "PM10 forecasting using soft computing techniques, " Res. J. Appl. Sci. Eng. Technol., vol 7, pp. 3253-3265, April 2014.
13. W. Sun, H. Zhang, A. Palazoglu, A. Singh, W. Zhang, S. Liu, "Prediction of 24-hour-Average PM2.5 concentration using a hidden Markov model with different emission distributions in Northern California " Sci. Total Environ., vol 443, pp. 93-103, January 2013.
14. M. Fu, W. Wang, Z. Le, M. Khorram, "Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model, " Neural Comput. Appl., vol 26, pp. 1789-1797, November 2015.
15. Y. Bai, Y. Li, X. X. Wang, J. J. Xie, C. Li, "Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions, " Atmos. Pollut. Res., vol 7, pp. 557-566, May 2016.
16. D. Dunea, A. Pohoata, S. Iordache, "Using wavelet-feedforward neural networks to improve air pollution forecasting in urban environments, " Environ. Monit. Assess, vol 187, pp. 477-492, July 2015.
17. S. Wu, Q. Feng, Y. Du, X. Li, "Artificial neural network models for daily PM10 air poiiution index prediction in the urban area of Wuhan, China, " Environ. Eng. Sci., vol 28, pp.357-363, April 2011.
18. D. Z. Antanasijevic, M. Ristic, A. Peric-Grujic, V. Pocajt, "Forecasting human exposure to PM10 at the national level using an artificial neural network apporach, " J. Chemom., vol 27, pp. 170-177, May 2013.
19. Y. Bai, Z. Z. Sun, B. Zeng, J. Deng, C. Li. "A multi-pattern deep fusion model for short-Term bus passenger flow forecasting, " Appl. Soft Comput., doi: 10.1016/j.asoc.2017.05.011, in press.
20. S. Roweis, L. Saul, "Nonlinear dimensionality reduction by locally linear embedding, " Sci., vol 290, pp. 2323-2326, December 2000.
21. Y. Bengio, A. Courville, and P. Vincent P, "Unsupervised feature learning and deep learning: A review and new perspectives, " Cornell University Library, 2014, arXiv: 1206.5538v3