A novel hybrid strategy for PM2.5 concentration analysis and prediction

Journal of Environmental Management

Volumn 196, Issue , 2017, Pages 443-457

  Jiang, Ping ^a   Dong, Qingli ^a   Li, Peizhi ^a  

^a DONGBEI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Adaptive fuzzy neural network; High dimension association rules; Learning vector quantization; Particle matter; Prediction

Indexed keywords

AGGLOMERATION; AIR POLLUTION; ASSOCIATION RULES; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; GEOGRAPHICAL REGIONS; PARTICLES (PARTICULATE MATTER); RIVER POLLUTION; SUPPORT VECTOR MACHINES; VECTOR QUANTIZATION;

ADAPTIVE-FUZZY-NEURAL-NETWORK; AIR POLLUTANTS; FUZZY NEURAL NETWORK MODEL; HIGH-DIMENSION ASSOCIATION RULE; HIGHER DIMENSIONS; HYBRID STRATEGIES; LEARNING VECTOR QUANTIZATION; PARTICLE MATTER; PM 2.5; URBAN AGGLOMERATIONS;

FORECASTING;

ATMOSPHERIC POLLUTION; CONCENTRATION (COMPOSITION); DESIGN; EARLY WARNING SYSTEM; ENVIRONMENTAL RESEARCH; FUZZY MATHEMATICS; PARTICULATE MATTER; PREDICTION; SUPPORT VECTOR MACHINE;

AIR POLLUTANT; CHINA; ERROR; EXPERIMENTAL MODEL; FUZZY LOGIC; HUMAN; LEARNING; LEAST SQUARE ANALYSIS; NERVOUS SYSTEM; PREDICTION; RIVER; SIMULATION; SUPPORT VECTOR MACHINE; AIR POLLUTION; ENVIRONMENTAL MONITORING; PARTICULATE MATTER;

CHINA; GUANGDONG; JINGJINJI; ZHUJIANG DELTA;

AIR POLLUTANTS; AIR POLLUTION; CHINA; ENVIRONMENTAL MONITORING; PARTICULATE MATTER;

EID: 85015946028     PISSN: 03014797     EISSN: 10958630     Source Type: Journal    
DOI: 10.1016/j.jenvman.2017.03.046     Document Type: Article

References (30)

1. Amirkhani, S., Nasirivatan, S., Kasaeian, a. B., Hajinezhad, a., ANN and ANFIS models to predict the performance of solar chimney power plants. Renew. Energy 83 (2015), 597–607.
2. Cheng, Y., Yu, W., Li, Q., GA-based multi-level association rule mining approach for defect analysis in the construction industry. Autom. Constr. 51 (2015), 78–91.
3. Cobourn, W.G., An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations. Atmos. Environ. 44 (2010), 3015–3023.
4. Develi, I., Sorgucu, U., Prediction of temperature distribution in human BEL exposed to 900 MHz mobile phone radiation using ANFIS. Appl. Soft Comput. J. 37 (2015), 1029–1036.
5. Diebold, F.X., Mariano, R.S., Comparing predictive accuracy. J. Bus. Econ. Stat. 13 (1995), 253–265.
6. Domańska, D., Wojtylak, M., Explorative forecasting of air pollution. Atmos. Environ., 92, 2014.
7. Dong, Q., Sun, Y., Li, P., A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: a case study of wind farms in China. Renew. Energy 102 (2017), 241–257.
8. Draxler, R., The accuracy of trajectories during ANATEX calculated using dynamic model analyses versus rawinsonde observations. J. Appl. Meteorol. 30 (1991), 1446–1467.
9. Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J., Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107 (2015), 118–128.
10. Feng, X., Li, Q., Zhu, Y., Wang, J., Liang, H., Xu, R., Formation and dominant factors of haze pollution over Beijing and its peripheral areas in winter. Atmos. Pollut. Res. 5 (2014), 528–538.
11. Hrust, L., Klaić, Z.B., Križan, J., Antonić, O., Hercog, P., Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmos. Environ. 43 (2009), 5588–5596.
12. Huang, W., Fan, H., Qiu, Y., Cheng, Z., Qian, Y., Application of fault tree approach for the causation mechanism of urban haze in Beijing-Considering the risk events related with exhausts of coal combustion. Sci. Total Environ. 544 (2016), 1128–1135.
13. Jang, J., Sun, C.T., E, M., Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence. Autom. Control. IEEE 42 (1997), 1482–1484.
14. Javadi, M., Ebrahimpour, R., Sajedin, A., Faridi, S., Zakernejad, S., Improving ECG classification accuracy using an ensemble of neural network modules. PLoS One, 6, 2011.
15. Jiawei Han, M.K.A.J.P., Data Mining: Concepts and Techniques. third ed., 2012, Books24x7. Morgan Kaufmann Publ., 745.
16. Keles, A., Kolcak, M., Keles, A., The adaptive neuro-fuzzy model for forecasting the domestic debt. Knowledge-Based Syst. 21 (2008), 951–957.
17. Library, W.E., National Bureau of Statistics of China. 2005.
18. Liu, J., Zuo, B., Zeng, X., Vroman, P., Rabenasolo, B., Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network. Expert Syst. Appl. 37 (2010), 2241–2246.
19. Perez, P., Reyes, J., An integrated neural network model for PM10 forecasting. Atmos. Environ. 40 (2006), 2845–2851.
20. Perez, P., Reyes, J., Prediction of maximum of 24-h average of PM10 concentrations 30 h in advance in Santiago, Chile. Atmos. Environ. 36 (2002), 4555–4561.
21. 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. 98 (2014), 665–675.
22. Rezaei, M., Valipour, M., Valipour, M., Modelling evapotranspiration to increase the accuracy of the estimations based on the climatic parameters. Water Conserv. Sci. Eng. 1 (2016), 1–11.
23. 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 118 (2015), 58–69.
24. Stern, R., Builtjes, P., Schaap, M., Timmermans, R., Vautard, R., Hodzic, A., Memmesheimer, M., Feldmann, H., Renner, E., Wolke, R., Kerschbaumer, A., A model inter-comparison study focussing on episodes with elevated PM10 concentrations. Atmos. Environ 42 (2008), 4567–4588.
25. Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., Liu, S., Prediction of 24-hour-average PM2.5 concentrations using a hidden Markov model with different emission distributions in Northern California. Sci. Total Environ. 443 (2013), 93–103.
26. Vasileva-Stojanovska, T., Vasileva, M., Malinovski, T., Trajkovik, V., An ANFIS model of quality of experience prediction in education. Appl. Soft Comput. J. 34 (2015), 129–138.
27. Vlachogianni, A., Kassomenos, P., Karppinen, A., Karakitsios, S., Kukkonen, J., Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. Sci. Total Environ. 409 (2011), 1559–1571.
28. Zhang, C., Ni, Z., Ni, L., Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors. Phys. A Stat. Mech. Appl. 438 (2015), 114–123.
29. Zheng, S.M., Yi, H.T., Li, H., The impacts of provincial energy and environmental policies on air pollution control in China. Renew. Sustain. Energy Rev. 49 (2015), 386–394.
30. Zhou, X., Cao, Z., Ma, Y., Wang, L., Wu, R., Wang, W., Concentrations, correlations and chemical species of PM2.5/PM10 based on published data in China: potential implications for the revised particulate standard. Chemosphere, 2016.