Abstract
The measurement of air pollution parameters is a costly process. Due to several reasons, the devices may not take measurements for certain days. In such cases robust estimation methods are quite necessary in order to fill the gaps in the time series. Artificial neural networks have been employed successfully for this purpose for hydrometeorological time series, as reported in literature. In this study, modelling of the time series of air pollution parameters was investigated using two ANN methods; a radial basis function algorithm (RBF) and feed forward back propagation method (FFBP). The ANN methods were employed to estimate the PM10 values using the NO and CO values. The data were from a measurement station in Istanbul, Turkey. The results of an initial statistical analysis were considered in the determination of the input layer node number. In the estimation study, values corresponding to other air pollution parameters were included in the input layer. The results were compared to those obtained with a conventional multi-linear regression (MLR) method.
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References
Abdul-Wahab, S A. and S. M. Al-Alawi; Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks, Environ. Model. Software 17 (2002) 219–228.
Aires, F., A. Chedin, N.A. Scott and W.B. Rossow; A regularized neural net approach for retrieval of atmospheric and surface temperatures with the IASI Instrument. J. Appl. Meteorol. 41 (2002) 144–159.
ASCE Task Committee; Artificial neural networks in hydrology I, J. Hydrol. Eng. 5 (2000) 115–123.
Bodri, L. and V. Cermak; Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia. Adv. Eng. Software 31 (2000) 211–221.
Broomhead, D. and D. Lowe; Multivariable functional interpolation and adaptive networks. Complex Syst. 2 (1988) 321–355.
Cigizoglu, H. K.; Incorporation of ARMA models into flow forecasting by artificial neural networks, Environmetrics 14 (2003a) 417–427.
Cigizoglu, H. K.; Estimation, forecasting and extrapolation of flow data by artificial neural networks. Hydrol. Sci. J. 48 (2003b) 349–361.
Cigizoglu, H. K.; Estimation and forecasting of daily suspended sediment data by multi layer perceptrons. Adv. Water Resour. 27 (2004) 185–195.
Cigizoglu, H.K., K. Alp and M. Kömürcü; Estimation of air pollution parameters using artificial neural networks, in: Advances in air pollution modelling for environmental security. NATO Advanced Research Workshop, 8–12 May 2004, Borovetz. Kluwer Academic Publisher, Dordrecht (2004).
Cigizoglu, H.K. and O. Kisi; Flow prediction by two back propagation techniques using k-fold partitioning of neural network training data. Nordic Hydrol. 36 (2005) (in press).
Duncan, K. and W.C. Potter; Ozone modeling using neural networks, J. Appl. Meteorol. 39 (2000) 291–296.
Gardner, M. and S. Dorling; Artificial neural network – derived trends in daily maximum surface ozone concentrations, J. Air Waste Manage. Assoc. 51 (2001) 1202–1210.
Hagan, M.T. and M.B. Menhaj; Training feedforward techniques with the Marquardt algorithm, IEEE Trans. Neural Networks 5 (1994) 989–993.
Kuligowski, R.J. and A.P. Barros; Combined IR-microwave satellite retrieval of temperature and dewpoint profiles using artificial neural networks, J. Appl. Meteorol. 40 (2001) 205–2067.
Luk, K.C., J.E. Ball and A. Sharma; A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting, J. Hydrol. 227 (2000) 56–65.
Maier, H.R. and G.C. Dandy; Neural network for the prediction and forecasting of water resources variables: a review of modeling issues and applications, Environ. Model. Software 15 (2000) 101–124.
McKendry, I.G.; Evaluation of artificial neural networks for fine paniculate pollution (PM10 and PM2.5) forecasting. J. Air Waste Manage. Assoc. 52 (2002) 1096–1101.
Narasimhan, R., J. Keller, G. Subramaniam, E. Raasch, B. Croley, K. Duncan and W.C. Potter; Ozone modeling using neural networks, J. Appl. Meteorol., 39 (2000) 291–296.
Sahai, A.K., M.K. Soman and V. Satyan; All India summer monsoon rainfall prediction using an artificial neural network, Clim. Dyn. 16 (2000) 291–302.
Silverman, D. and J.A. Dracup; Artificial neural networks and long-range precipitation prediction in California, J. Appl. Meteorol. 39 (2000) 57–66.
Seinfeld, J.H. and S. N. Pandis; Atmospheric chemistry and physics, John Wiley & Sons Inc., New York 1998,
U.S. EPA (Environmental Protection Agency); Latest findings on national air quality: 2002 status and trends, http://www.epa.gov/airtrends, accessed on 15th February 2004.
Wayne, R.P.; Chemistry of atmospheres, Oxford University Press, New York 2000.
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Cigizoglu, H.K., Alp, K., Kömürcü, M. (2006). Two Neural Network Methods in Estimation of Air Pollution Time Series. In: Barnes, I., Rudzinski, K.J. (eds) Environmental Simulation Chambers: Application to Atmospheric Chemical Processes. Nato Science Series: IV: Earth and Environmental Science, vol 62. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4232-9_36
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DOI: https://doi.org/10.1007/1-4020-4232-9_36
Publisher Name: Springer, Dordrecht
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