Reliable prediction of carbon monoxide using developed support vector machine

Atmospheric Pollution Research

Volumn 7, Issue 3, 2016, Pages 412-418

  Moazami, Saber ^a   Noori, Roohollah ^b   Amiri, Bahman Jabbarian ^b   Yeganeh, Bijan ^c   Partani, Sadegh ^d   Safavi, Salman ^e  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b UNIVERSITY OF TEHRAN   (Iran)

^c QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^d ISLAMIC AZAD UNIVERSITY   (Iran)

^e ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Artificial intelligence; Carbon monoxide; Support vector machine; Uncertainty

Indexed keywords

EID: 84964956017     PISSN: 13091042     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apr.2015.10.022     Document Type: Article

References (44)

1. Abbaspour K., Yang J., Maximov I., Siber R., Bogner K., Mieleitner J., Zobrist J., Srinivasan R. Modeling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J. Hydrol. 2007, 333(2-4):413-430.
2. Abe S. Support Vector Machines for Pattern Classification 2005, Springer-Verlag, London.
3. Aqil M., Kita I., Yano A., Nishiyama S. Analysis and prediction of flow from local source in a river basin using a neuro-fuzzy modeling tool. J. Environ. Manag. 2007, 85(1):215-223.
4. Bayat R. Source Apportionment of Tehran's Air Pollution 2005, Department of Civil and Environmental Engineering, Sharif University of Technology, Tehran, Iran, (M.Sc thesis).
5. Bergin M.S., Noblet G.S., Petrini K., Dhieux J.R., Milford J.B., Harley R.A. Formal uncertainty analysis of a lagrangian photochemical air pollution model. Environ. Sci. Technol. 1999, 33(7):1116-1126.
6. 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. 1995, 34(8):1848-1862.
7. Coulibaly P., Anctil F., Bobee B. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J. Hydrol. 2000, 230(3-4):244-257.
8. Dabberdt W.F., Miller E. Uncertainty, ensembles, and air quality dispersion modeling: applications and challenges. Atmos. Environ. 2000, 34(27):4667-4673.
9. Duci A., Papakonstantinou K., Chaloulakou A., Markatos N. Numerical approach of carbon monoxide concentration dispersion in an enclosed garage. Build. Environ. 2004, 39(9):1043-1048.
10. Elangasinghe M.A., Singhal N., Dirks K.N., Salmond J.A. Development of an ANN-based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos. Pollut. Res. 2014, 5:696-708.
11. Fisher B.E.A., Ireland M. Using fuzzy numbers to treat predictions from more than one model. 25th NATO/CCMS International Technical Meeting on Air Pollution and its Application, Louvain-la-Neuve, Belgium 2001.
12. Fox D.G. Uncertainty in air quality modeling. Bull. Am. Meteorol. Soc. 1984, 65(1):27-36.
13. Goel A., Pal M. Application of support vector machines in scour prediction on grade-control structures. Eng. Appl. Artif. Intell. 2009, 22(2):216-223.
14. 10 hourly concentrations, in the Greater Area of Athens, Greece. Atmos. Environ. 2006, 40(7):1216-1229.
15. Hsu C.W., Chang C.C., Lin C.J. A Practical Guide to Support Vector Classification 2003, http://www.csie.ntu.edu.tw/%7Ecjlin/papers/guide/guide.pdf.
16. 10 dispersion over Pune, India. Atmos. Environ. 2007, 41(9):1976-1988.
17. Lu W., Wang W. Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. Chemosphere 2005, 59:693-701.
18. Lumbreras J., García-Martos C., Mira J., Borge R. Computation of uncertainty for atmospheric emission projections from key pollutant sources in Spain. Atmos. Environ. 2009, 43(8):1557-1564.
19. Manomaiphiboon K., Russell A.G. Effects of uncertainties in parameters of a Lagrangian particle model on mean ground-level concentrations under stable conditions. Atmos. Environ. 2004, 38(33):5529-5543.
20. Markakis K., Valari M., Perrussel O., Sanchez O., Honore C. Climate forced air-quality modeling at urban scale: sensitivity to model resolution, emissions and meteorology. Atmos. Chem. Phys. 2015, 15:4767-4821.
21. Nagendra S.M.S., Khare M. Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecol. Model. 2006, 190(1):99-115.
22. Nagendra S.M.S., Khare M. Artificial neural network based carbon monoxide persistence models for episodic urban air quality management. Environ. Monit. Assess. 2008, 139(1-3):247-255.
23. Noori R., Hoshyaripour G., Ashrafi K., Nadjar-Araabi B. Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmos. Environ. 2010, 44:476-482.
24. Noori R., Karbassi A.R., Moghaddamnia A., Han D., Zokaei-Ashtiani M.H., Farokhnia A., Ghafari Gousheh M. Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. J. Hydrol. 2011, 401(3):177-189.
25. 5 monitoring: uncertainty analysis. Environ. Prog. Sustain. Energy 2013, 32(2):344-349.
26. Noori R., Hoshyaripour G., Ashrafi K., Rasti O. Introducing an appropriate model using support vector machine for predicting carbon monoxide daily concentration in Tehran atmosphere. Iran. J. Health Environ. 2013, 6(1):1-10.
27. Noori R., Deng Z., Kiaghadi A., Kachoosangi F.T. How reliable are ANN, ANFIS, and SVM techniques for predicting longitudinal dispersion coefficient in natural rivers?. J. Hydraul. Eng. 2015, 10.1061/(ASCE)HY.1943-7900.0001062.
28. Pai T.Z., Chen L. Comparisons of GM (1, 1), and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan. Atmos. Pollut. Res. 2015, 6:572-580.
29. Pai T.Z., 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. Water Air Soil Pollut. 2011, 217(1-4):25-33.
30. Pai T.Z., Hanaki K., Chiou R.J. Forecasting hourly roadside particulate matter in Taipei County of Taiwan based on first-order and one-variable Grey Model. Clean Soil Air Water 2013, 41(8):737-742.
31. Pal M., Goel A. Prediction of end-depth-ratio and discharge in trapezoidal shaped channels using support vector machines. Water Resour. Manag. 2007, 21:1763-1780.
32. Pal M., Singh N.K., Tiwari N.K. Support vector regression based modeling of pier scour using field data. Eng. Appl. Artif. Intell. 2011, 24(5):911-916.
33. Papakonstantinou K., Chaloulakou A., Duci A., Vlachakis N., Markatos N. Air quality in an underground garage: computational and experimental investigation of ventilation effectiveness. Energy Build. 2003, 35(9):933-940.
34. Rosa I.M.D., Pereira J.M.C., Tarantola S. Atmospheric emissions from vegetation fires in Portugal (1990-2008): estimates, uncertainty analysis, and sensitivity analysis. Atmos. Chem. Phys. 2011, 11:2625-2640.
35. Russo A., Lind P.L., Raischell F., Trigol R., Mende M. Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales. Atmos. Pollut. Res. 2015, 6:540-549.
36. Shahraiyni H.T., Shahsavani D., Sargazi S., Habibi-Nokhanda M. Evaluation of MARS for the spatial distribution modeling of carbon monoxide in an urban area. Atmos. Pollut. Res. 2015, 6:581-588.
37. Singh K.P., Gupta S., Kumar A., Shukla S.P. Linear and nonlinear modeling approaches for urban air quality prediction. Sci. Total Environ. 2012, 426:244-255.
38. Singh K.P., Gupta S., Rai P. Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos. Environ. 2013, 80:426-437.
39. Sousa S.I.V., Martins F.G., Alvim-Ferraz M.C.M., Pereira M.C. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ. Model. Softw. 2007, 22:97-103.
40. Sousa S.I.V., Pires J.C.M., Martins F.G., Pereira M.C., Alvim-Ferraz M.C.M. Potentialities of quantile regression to predict ozone concentrations. Environmetrics 2009, 20(2):147-158.
41. Vapnik V.N. Statistical Learning Theory 1998, Wiley, New York.
42. Vardoulakis S., Fisher B.E.A., Gonzalez-Flesca N., Pericleous K. Model sensitivity and uncertainty analysis using roadside air quality measurements. Atmos. Environ. 2002, 36(13):2121-2134.
43. Wang X.X., Chen S., Lowe D., Harris C.J. Sparse support vector regression based on orthogonal forward selection for the generalised kernel model. Neurocomputing 2006, 70(1-3):462-474.
44. Zhao C., Chen S., Leung L.R., Qian Y., Kok J.F., Zaveri R.A., Huang J. Uncertainty in modeling dust mass balance and radiative forcing from size parameterization. Atmos. Chem. Phys. 2013, 13:10733-10753.