A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants' Concentration

Environmental Modeling and Assessment

Volumn 16, Issue 5, 2011, Pages 503-517

  Prakash, Amit ^a   Kumar, Ujjwal ^b   Kumar, Krishan ^a   Jain, V K ^a  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

^b FLEMISH INSTITUTE FOR TECHNOLOGICAL RESEARCH VITO   (Belgium)

Author keywords

Air pollution forecasting; Elman network; Neural network; Wavelet transformation

Indexed keywords

AIR QUALITY; ERRORS; FORECASTING; MEAN SQUARE ERROR; NEURAL NETWORKS; NITROGEN OXIDES; SULFUR DIOXIDE; WAVELET TRANSFORMS;

AIR POLLUTION FORECASTING; AIR QUALITY FORECASTING; ELMAN NETWORK; MEAN ABSOLUTE PERCENTAGE ERROR; RECURRENT NEURAL NETWORK MODEL; ROOT MEAN SQUARE ERRORS; WAVELET TRANSFORMATIONS; WAVELET-BASED NEURAL NETWORK;

RECURRENT NEURAL NETWORKS;

AIR QUALITY; AMBIENT AIR; ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC POLLUTION; CONCENTRATION (COMPOSITION); NUMERICAL MODEL; PREDICTION; URBAN ATMOSPHERE; WAVELET ANALYSIS;

DELHI; INDIA;

EID: 80052290632     PISSN: 14202026     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10666-011-9270-6     Document Type: Article

References (40)

1. Aquino, V. A., Trevino, E. S. G., Romero, R. R., Cruz, J. F. R., Ojeda, G. G.,Asomoza, J. R. (2005). Wavelet-network based on L1-norm minimization for learning chaotic time series. Journal of Applied Research and Technology, 3, 211-221.
2. Bruce, A. G.,Goa, H.-Y. (1996). Understanding waveShrink: variance and bias estimation. Biometrika, 83, 727-745.
3. Chaloulakou, A., Saisana, M.,Spyrellis, N. (2003). Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Science of the Total Environment, 313, 1-13.
4. Chelani, A. B., Chalapati Rao, C. V., Phadke, K. M.,Hasan, M. Z. (2002). Prediction of sulphur dioxide concentration using artificial neural networks. Environmental Modelling and Software, 17, 161-168.
5. Chelani, A. B.,Devotta, S. (2007). Air quality assessment in Delhi: Before and after CNG as fuel. Environmental Monitoring and Assessment, 125, 257-263.
6. Cogliani, E. (2001). Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables. Atmospheric Environment, 35, 2871-2877.
7. Cornish, C. (2006). The WMTSA wavelet toolkit for MATLAB. http://www. atmos. washington. edu/~wmtsa/.
8. Coulibaly, P., Anctil, F., Aravena, R.,Bobee, B. (2001). Artificial neural network modeling of water table depth fluctuations. Water Resources Research, 37(4), 885-896.
9. Elkamel, A., Abdul-Wahab, S., Bouhamra, W.,Alper, E. (2001). Measurements and prediction of ozone levels around a heavily industrialized area: A neural network approach. Advances in Environmental Research, 5, 47-59.
10. Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7, 195-224.
11. Galvao, R. K. H.,Yoneyama, T. (2004). A competitive neural network for signal clustering. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 34, 1282-1288.
12. Gardner, M. W.,Dorling, S. R. (1998). Artificial neural network (the multi layer perception)-a review of applications in the atmospheric sciences. Atmospheric Environment, 32, 2627-2636.
13. Gautam, A. K., Chelani, A. B., Jain, V. K.,Devotta, S. (2008). A new scheme to predict chaotic time series of air pollutant concentrations using artificial neural network and nearest neighbor searching. Atmospheric Environment, 42, 4409-4417.
14. Gokhale, S. Khare, M. (2007). Statistical behaviour of carbon monoxide from vehicular exhausts in urban environment. Environment Modeling and Software, 22, 526-535.
15. Goyal, P. Rama Krishna, T. V. B. P. S. (1998). Various methods of emission estimation of vehicular exhausts in urban environment. Environment Modeling and Software, 22, 526-535.
16. Grinsted, A., Moore, J. C. Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11, 561-566.
17. Hardle, W., Kerkyacharian, G., Picard, D. Tsybakov, A. (1998). Wavelets, approximation, and Statistical applications (Lecture Notes in Statistics, vol 129). New York: Springer.
18. Ho, S. L., Xie, M. Goh, T. N. (2002). A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Computers and Industrial Engineering, 42, 371-375.
19. Hogrefe, C., Vempaty, S., Rao, S. T. Porter, P. S. (2003). A comparison of four techniques for separating different time scales in atmospheric variables. Atmospheric Environment, 37, 313-325.
20. Hogrefe, C., Biswas, J., Lynn, B. H., Civerolo, K., Ku, J. Y., Rosenthal, J., Rosenzweig, C., Goldberg, R. Kinney, P. (2004). Simulating regional-scale ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment, 38, 2627-2638.
21. Iyengar, S. S., Cho, E. C. Phoha, V. V. (2002). Foundations of wavelet networks and applications. USA: Chapman Hall, CRC.
22. Kaastra, I. Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10, 215-236.
23. Karppinen, A., Kukkonen, J., Eloladie;hde, T., Konttinen, M., Koskentalo, T. Rantakrans, E. (2000). A modelling system for predicting urban air pollution: Model description and applications in the Helsinki metropolitan area. Atmospheric Environment, 34, 3723-3733.
24. Kumar, U., Prakash, A. Jain, V. K. (2008). Characterization of chaos in air pollutants: A Volterra-Wiener-Korenberg series and numerical titration approach. Atmospheric Environment, 42, 1537-1551.
25. Lau, K.-M. Weng, H. (1995). Climate signal detection using wavelet transform: How to make a time series sing. Bulletin of the American Meteorological Society, 76, 2391-2402.
26. x concentrations in south Taiwan. Atmospheric Environment, 37, 2051-2059.
27. Maqsood, I., Muhammad, R. K. Abraham, A. (2002). Neurocomputing based Canadian weather analysis (pp. 39-44). USA: Computational Intelligence and Applications, Dynamic Publishers.
28. Meyers, S. D., Kelly, B. G. O'Brien, J. J. (1993). An introduction to wavelet analysis in oceanography and meteorology: With application to the dispersion of Yanai waves. Monthly Weather Review, 121, 2858-2866.
29. Osowski, S. Garanty, K. (2007). Forecasting of the daily meteorological pollution using wavelets and support vector machine. Engineering Applications of Artificial Intelligence, 20, 745-755.
30. Percival, D. B. Walden, A. T. (2000). Wavelet methods for time series analysis. UK: Cambridge University Press.
31. 2 concentrations near a street with heavy traffic in Santiago, Chile. Atmospheric Environment, 35, 1783-1789.
32. Podnar, D., Koracin, D. Panorska, A. (2002). Application of artificial neural network to modeling the transport and dispersion of tracers in complex terrain. Atmospheric Environment, 36, 561-570.
33. Rao, S. T., Zurbenko, I. G., Neagu, R., Porter, P. S., Ku, V. Henry, R. F. (1997). Space and time scales in ambient ozone data. Bulletin of the American Meteorological Society, 78, 2153-2166.
34. Reich, S. L., Gomez, D. R. Dawidowski, L. E. (1999). Artificial neural network for the identification of unknown air pollution sources. Atmospheric Environment, 33, 3045-3052.
35. Rying, E. A., Bilbro, G. L. Lu, J.-C. (2002). Focused local learning with wavelet neural networks. IEEE Transactions on Neural Networks, 13, 304-319.
36. Willmot, C. J., Ackleson, S. G., Davis, R. E., Feddema, J.-J., Klink, K. M., Legates, D. R., et al. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research, 90, 8995-9005.
37. Willmott, C. J. (1981). On the validation of models. Physical Geography, 2, 184-194.
38. Wong, L. T., Mui, K. W. Hui, P. S. (2006). A statistical model for characterizing common air pollutants in air conditioned offices. Atmospheric Environment, 40, 4246-4257.
39. Zaharim, A., Shaharuddin, M., Jailani, M., Nor, M., Karim, O. A. Karim, S. (2009). Relationships between Airborne particulate matter and meteorological variables using non-decimated wavelet transform. European Journal of Scientific Research, 27(2), 308-312.
40. 2 concentrations. In R. G. Ingalls, M. D. Rossetti, J. S. Smith B. A. Peters (Eds.), Proceedings of the 2004 winter simulation conference, (pp. 1770-1778). doi: 10. 1109/WSC. 2004. 1371529.