A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

Atmospheric Environment

Volumn 134, Issue , 2016, Pages 168-180

  Niu, Mingfei ^a   Wang, Yufang ^a   Sun, Shaolong ^b   Li, Yongwu ^b  

^a LANZHOU UNIVERSITY   (China)

^b CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Complementary ensemble empirical mode decomposition; Grey wolf optimizer; Hybrid decomposition ensemble model; PM2.5 concentration forecasting; Support vector regression

Indexed keywords

ARTIFICIAL INTELLIGENCE; OPTIMIZATION; SIGNAL PROCESSING;

DECOMPOSITION METHODS; DIRECTIONAL PREDICTIONS; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENSEMBLE MODELING; INTRINSIC MODE FUNCTIONS; OPTIMIZERS; PREDICTION ACCURACY; SUPPORT VECTOR REGRESSION (SVR);

FORECASTING;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL INTELLIGENCE; ATMOSPHERIC MODELING; BENCHMARKING; CONCENTRATION (COMPOSITION); DECOMPOSITION ANALYSIS; EMPIRICAL ANALYSIS; ENSEMBLE FORECASTING; PARTICULATE MATTER; PREDICTION; REGRESSION ANALYSIS;

ALGORITHM; ARTICLE; COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION; DECOMPOSITION; DECOMPOSITION AND ENSEMBLE MODEL; EMPIRICISM; FORECASTING; GREY WOLF OPTIMIZER; HEURISTICS; PARTICULATE MATTER; PRECIPITATION; PREDICTION; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE; TEMPERATURE; WHITE NOISE;

EID: 84962448935     PISSN: 13522310     EISSN: 18732844     Source Type: Journal    
DOI: 10.1016/j.atmosenv.2016.03.056     Document Type: Article

References (33)

1. 10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci. Total Environ. 2013, 443:511-519.
2. 10 concentration forecast model based on stepwise regression and wavelet analysis. Atmos. Environ. 2013, 74:346-359.
3. Díaz-Robles L.A., Ortega J.C., Fu J.S., Reed G.D., Chow J.C., Watson J.G., Moncada-Herrera J.A. A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco. Chile. Atmos. Environ. 2008, 42:8331-8340.
4. 2.5 modelling in naturally ventilated schools buildings. Atmos. Environ. 2014, 94:11-21.
5. El-Fergany A.A., Hasanien H.M. Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms. Elect. Power Compon. Syst. 2015, 43:1548-1559.
6. Emary E., Zawbaa H.M., Hassanien A.E. Binary grey wolf optimization approaches for feature selection. Neurocomputing 2016, 172:371-381.
7. 10 in metropolitan areas: efficacy of neural networks. Environ. Pollut. 2012, 163:62-67.
8. Jian L., Zhao Y., Zhu Y.P., Zhang M.B., Bertolatti D. An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci. Total Environ. 2012, 426:336-345.
9. Juneng L., Latif M.T., Tangang F. Factors influencing the variations of PM10 aerosol dust in Klang Valley, Malaysia during the summer. Atmos. Environ. 2011, 45:4370-4378.
10. 10 forecasting in Europe. Atmos. Environ. 2009, 43:6425-6434.
11. Lin K.P., Pai P.F., Yang S.L. Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms. Appl. Math. Comput. 2011, 217:5318-5327.
12. Lu W.Z., Wang W.J. Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. Chemosphere 2005, 59:693-701.
13. Mirjalili S., Mirjalili S.M., Lewis A. Grey wolf optimizer. Adv. Eng. Softw. 2014, 69:46-61.
14. Niu M., Sun S., Wu J., Yu L., Wang J. An innovative integrated model using the singular spectrum analysis and nonlinear multi-layer perceptron network optimized by hybrid intelligent algorithm for short-term load forecasting. Appl. Math. Model 2015, 40:4079-4093.
15. Niu M., Sun S., Wu J., Zhang Y. Short-Term wind speed hybrid forecasting model based on bias correcting study and its application. Math. Probl. Eng. 2015, 2015:13. Article ID 351354. 10.1155/2015/351354.
16. Pai T.Y., Ho C.L., Chen S.W., Lo H.M., Sung P.J., Lin S.W., Lai W.J., Tseng S.C., Ciou S.P., Kuo J.L., Kao J.T. Using seven types of GM (1, 1) model to forecast hourly particulate matter concentration in Banciao city of Taiwan. Watera Air Soil. Pollut. 2011, 217:25-33.
17. 10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management. Environ. Sci. Pollut. Res. 2011, 18:316-327.
18. Poggi J.M., Portier B. PM10 forecasting using clusterwise regression. Atmos. Environ. 2011, 45:7005-7014.
19. Qin S., Liu F., Wang J., Sun B. Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmos. Environ. 2014, 98:665-675.
20. Song Y., Qin S., Qu J., Liu F. The forecasting research of early warning systems for atmospheric pollutants: a case in Yangtze River Delta region. Atmos. Environ. 2015, 118:58-69.
21. Suárez Sánchez A., García Nieto P.J., Riesgo Fernández P., Del Coz Díaz J.J., Iglesias-Rodríguez F.J. Application of a SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math. Comput. Model 2011, 54:1453-1466.
22. Sulaiman M.H., Mustaffa Z., Mohamed M.R., Aliman O. Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl. Soft Comput. 2015, 32:286-292.
23. Tang L., Yu L., Wang S., Li J., Wang S.Y. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting. Appl. Energy 2012, 93:432-443.
24. Tang L., Dai W., Yu L., Wang S. A novel CEEMD-based EELM ensemble learning paradigm for crude oil price forecasting. Int. J. Info. Tech. Dec. Mak. 2015, 14:141-169.
25. 2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Total Environ. 2011, 409:1266-1276.
26. Wang J., Wang J., Li Y., Zhu S., Zhao J. Techniques of applying wavelet de-noising into a combined model for short-term load forecasting. Int. J. Electr. Power Energy Syst. 2014, 62:816-824.
27. 2 daily concentrations. Sci. Total Environ. 2015, 505:1202-1212.
28. Wu Z., Huang N.E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 2008, 1:1-41.
29. Yeh J.R., Shieh J.S., Huang N.E. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2010, 2:135-156.
30. Yu L., Wang S.Y., Lai K.K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ. 2008, 30:2623-2635.
31. Yu L., Wang Z., Tang L. A decomposition-ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting. Appl. Energy 2015, 156:251-267.
32. Zhang H., Zhang W., Palazoglu A., Sun W. Prediction of ozone levels using a hidden markov model (HMM) with gamma distribution. Atmos. Environ. 2012, 62:64-73.
33. Zhang X., Wang P., Liang D., Fan C., Li C. A soft self-repairing for FBG sensor network in SHM system based on PSO-SVR model reconstruction. Opt. Commun. 2015, 343:38-46.