Identification of the best architecture of a multilayer perceptron in modeling daily total ozone concentration over Kolkata, India

• Autorzy: De, S. S. , De, B.K. , Chattopadhyay, G. , Paul, S. , Haldar, D.K. , Chakrabarty, D.K.
• Języki publikacji: EN

Abstrakty

EN

Autoregressive neural network (AR-NN) models of various orders have been generated in this work for the daily total ozone (TO) time series over Kolkata (22.56°N, 88.5°E). Artificial neural network in the form of multilayer perceptron (MLP) is implemented in order to generate the AR-NN models of orders varying from 1 to 13. An extensive variable selection method through multiple linear regression (MLR) is implemented while developing the AR-NNs. The MLPs are characterized by sigmoid non-linearity. The optimum size of the hidden layer is identified in each model and prediction are produced by validating it over the test cases using the coefficient of determination (R²) and Willmott’s index (WI). It is observed that AR-NN model of order 7 having 6 nodes in the hidden layer has maximum prediction capacity. It is further observed that any increase in the orders of AR-NN leads to less accurate prediction.

Słowa kluczowe

EN

autoregressive neural network; daily total ozone; multilayer perceptron; coefficient of determination; Willmott's index

• Czasopismo: Acta Geophysica
• Rocznik: 2011
• Tom: Vol. 59, no. 2
• Strony: 361-376
• Opis fizyczny: Bibliogr. 64 poz.

Twórcy

autor

De, S. S.

autor

De, B.K.

autor

Chattopadhyay, G.

autor

Paul, S.

autor

Haldar, D.K.

autor

Chakrabarty, D.K.

• Centre of Advanced Study in Radio Physics and Electronics, University of Calcutta, Kolkata, India,

Bibliografia

• Abdollahian, M., R. Foroughi, and N. Debnath (2006), Optimal statistical model for forecasting ozone, J. Comput. Meth. Sci. Eng. 6, Suppl. 2, 321-336.
• Aksoy, B., S. Incecik, S. Topcu, D. Demirhan Bari, C. Kahya, Y. Acar, M. Ozunlu, and M. Ekici (2009), Total ozone over Ankara and its forecasting Rusing regression models, Int. J. Remote Sens. 30, 4387-4400.
• Allen, D.R., and R.A. Reck (1997), Daily variation in TOMS total ozone data, J. Geophys. Res. 102, D12, 13603-13608.
• Badescu, V. (1994), Verification of a simple relationship of cloud shade to point cloudiness, Renew. Energ. 5, 1503-1505.
• Bandyopadhyay, G., and S. Chattopadhyay (2007), Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone, Int. J. Environ. Sci. Tech. 4, 141-149.
• Bandyopadhyay, G., and S. Chattopadhyay (2008), A probe into the chaotic nature of total ozone time series by correlation dimension method, Soft Comput. 12, 1007-1012.
• Benvenuto, F., and A. Marani (2000), Neural networks for environmental problems: Data quality control and air pollution nowcasting, Global Nest: Int. J. 2, 3, 281-292.
• Cannon, A.J., and P.H. Whitfield (2002), Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models, J. Hydrol. 259, 136-151.
• Cartalis, C., and C. Varotsos (1994), Surface ozone in Athens, Greece, at the beginning and at the end of the 20th century, Atmos. Environ. 28, 3-8.
• Chattopadhyay, G., and S. Chattopadhyay (2009a), Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach, Comput. Geosci. 35, 1925-1932.
• Chattopadhyay, G., and S. Chattopadhyay (2009b), Predicting daily total ozone over Kolkata, India: Skill assessment of different neural network models, Meteorol. Appl. 16, 179-190.
• Chen, J.L., S. Islam, and P. Biswas (1998), Nonlinear dynamics of hourly ozone concentrations: nonparametric short term prediction, Atmos. Environ. 32, 1839-1848.
• Claude, H., U. Kohler, and W. Steinbrecht (2004), Evolution of ozone and temperature trends at Hohenpeissenberg (Germany). In: C.S. Zerefos (ed.), Proc. XX Quadrennial Ozone Symposium, 1-8 June 2004, Kos, Greece, 314-315.
• Comrie, A.C. (1997), Comparing neural networks and regression models for ozone forecasting, J. Air Waste Manage. 47, 360-363.
• Corani, G. (2005), Air quality prediction in Milan: Feed-forward neural networks, pruned 16 neural networks and lazy learning, Ecol. Model. 185, 513-529.
• Demir, G., G. Altay, C.O. Sakar, S. Albayrak, H. Ozdemir, and S. Yalcin (2008), Prediction and evaluation of tropospheric ozone concentration in Istanbul using artificial neural network modeling according to time parameter, J. Sci. Ind. Res. India 67, 674-679.
• Dorffner, G. (1996), Neural network for time series processing, Neural Netw. World 6, 447-468.
• Errera, Q., and D. Fonteyn (2001), Four-dimensional variational chemical assimilation of CRISTA stratospheric measurements, J. Geophys. Res. 106, D11, 12253-12266.
• Eskes, H., A. Segers, and P. van Velthoven (2005), Ozone forecasts of the stratospheric polar vortex-splitting event in September 2002, J. Atmos. Sci. 62, 812-821.
• Gardner, M.W., and S.R. Dorling (1998), Artificial neural networks – the multilayer perceptron: A review of applications in atmospheric sciences, Atmos. Environ. 32, 2627-2636.
• Gilleland, E., and D. Nychka (2005), Statistical models for monitoring and regulating ground-level ozone, Environmetrics 16, 535-546.
• Graf-Jacottet, M., and M.H. Jaunin (1998), Predictive models for ozone and nitro gen dioxide time series, Environmetrics 9, 393-406.
• Hansen, G., and T. Svenøe (2005), Multilinear regression analysis of the 65-year Tromsø total ozone series, J. Geophys. Res. 110, D10103.
• Hauglustaine, D., L. Emmons, M. Newchurch, G. Brasseur, T. Takao, K. Matsubara, J. Johnson, B. Ridley, J. Stith, and J. Dye (2001), On the role of lighting NOx in the formation of tropospheric ozone plumes: A global model perspective, J. Atmos. Chem. 38, 277-294.
• Hrust, L., Z.B. Klaić, V. Križan, O. Antonić, and P. Hercog (2009), Neural Network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations, Atmos. Environ. 43, 35, 5588-5596.
• Jacob, D.J. (2000), Heterogeneous chemistry and tropospheric ozone, Atmos. Environ. 34, 2131-2159.
• Kamarthi, S.V., and S. Pittner (1999), Accelerating neural network training using weight extrapolation, Neural Networks 12, 9, 1285-1299.
• Koçak, K., L. Saylan, and O. Sen (2000), Nonlinear time series prediction of O3 concentration in Istanbul, Atmos. Environ. 34, 8, 1267-1271.
• Kondratyev, K.Y., and C. Varotsos (2000), Atmospheric Ozone Variability: Implications for Climate Change, Human Health and Ecosystems, Springer, Chichester.
• Kondratyev, K.Y., and C.A. Varotsos (2001a), Global tropospheric ozone Dynamics-part I: Tropospheric ozone precursors, Environ. Sci. Pollut. Res. 8, 57- 62.
• Kondratyev, K.Y., and C.A. Varotsos (2001b), Global tropospheric ozone Dynamics-part II: Numerical modeling of tropospheric ozone variability, Environ. Sci. Pollut. Res. 8, 113-119.
• Lahoz, W.A., Q. Errera, R. Swinbank, and D. Fonteyn (2007), Data assimilation of stratospheric constituents: a review, Atmos. Chem. Phys. 7, 5745-5773.
• Massart, B.G.J., and O.M. Kvalheim (1998), Ozone forecasting from meteorological variables – Part I: Predictive models by moving window and partial east squares regression, Chemometr. Intell. Lab. 42, 179-190.
• Monge Sanz, B.M., and N.J. Medrano Marqués (2004), Total ozone time series analysis: A neural network model approach, Nonlinear Proc. Geophys. 11, 683-689.
• Monge Sanz, B., and N. Medrano Marques (2003), Artificial neural networks applications for total ozone time series. In: M.J. A´lvarez Jr. (ed.), Proc. 7th Inter. Work-Conference on Artificial and Natural Neural Networks, IWANN, Menorca, Spain, June 2003, Springer, Berlin, 806-813.
• Muller, M.D., A.K. Kaifel, M. Weber, S. Tellmann, J.P. Burrows, and D. Loyola (2003), Ozone profile retrieval from Global Ozone Monitoring Experiment (GOME) data using a neural network approach (Neural Network Ozone Retrieval System (NNORSY)), J. Geophys. Res. 108, D16, 4497.
• NASA (1998), Earth Probe Total Ozone Mapping Spectrometer (TOMS) Data Products User's Guide, NASA Technical Publication 20771, Goddard Space Flight Center Greenbelt, Maryland.
• Nunnari, G., A.F.M. Nucifiora, and C. Radineri (1998), The application of neural techniques to the modelling of time series of atmospheric pollution data, Ecol. Model. 111, 187-205.
• Oliveira, K.A. de, Á. Vannucci, and E.C. da Silva (2000), Using artificial neural networks to forecast chaotic time series, Physica A 284, 393-404.
• Pastor-Bárcenas, O., E. Soria-Olivas, J.D. Martín-Guerrero, G. Camps-Valls, J.L. Carrasco-Rodríguez, and S. del Valle-Tascón (2005), Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modelling, Ecol. Model. 182, 149-158.
• Peréz, P., A. Trier, and J. Reyes (2000), Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile, Atmos. Environ. 34, 1189-1196.
• Principe, J.C., A. Rathie, and J.M. Kuo (1992), Prediction of chaotic time series with neural networks and the issue of dynamic modeling, Int. J. Bifurcat. Chaos 2, 989-996.
• Prybutok, R., Y. Junsub, and D. Mitchell (2000), Comparison of neural Network models with ARIMA and regression models for prediction of Houston’s daily maximum ozone concentrations, Euro. J. Operat. Res. 122, 31-40.
• Rojas, R. (1996), Neural Networks: A Systematic Introduction, Springer, New York.
• Rubin, M.B. (2001), The history of ozone. The Schönbein period, 1839-1868, Bull. Hist. Chem. 26, 40-56.
• Seinfeld, J.H., and S.N. Pandis (1998), Atmospheric Chemistry and Physics – From Air Pollution to Climate Change, John Wiley & Sons, New York.
• Silverman, D., and J.A. Dracup (2000), Artificial neural networks and longrange precipitation prediction in California, J. Appl. Meteorol. 39, 57-66.
• Sivakumar, B., R. Berndtsson, and U. Lall (2004), Preface: Nonlinear deterministic dynamics in hydrologic systems: Present activities and future challenges, Nonlinear Proc. Geoph. 11, 1-2, http://www.nonlin-processes-geophys.net/ prefaces/preface49.pdf.
• Soukharev, B.E., and L.L. Hood (2006), Solar cycle variation of stratospheric ozone: Multiple regression analysis of long-term satellite data sets and comparisons with models, J. Geophys. Res. 111, D20314.
• Sousa, S.I.V., F.G. Martins, M.C. Pereira, and M.C.M. Alvim-Ferraz (2006), Prediction of ozone concentrations in Oporto city with statistical approaches, Chemosphere 64, 7, 1141-1149.
• Struthers, H., R. Brugge, W.A. Lahoz, A. O’Neill, and R. Swinbank (2002), Assimilation of ozone profiles and total column measurements into a global general circulation model, J. Geophys. Res. 107, D20, 4438.
• Thompson, A.M., J.C. Witte, R.D. Hudson, H. Guo, J.R. Herman, and M. Fujiwara (2001), Tropical tropospheric ozone and biomass burning, Science 291, 2128-2132.
• Varotsos, C. (2005), Power-law correlations in column ozone over Antarctica, Int. J. Remote Sens. 26, 3333-3342.
• Varotsos, C., and D. Krik-Davidoff (2006), Long-memory processes in ozone and temperature variations at the region 60 degrees S – 60 degrees N, Atmos. Chem. Phys. 6, 4093-4100.
• Varotsos, C., D. Alexandris, G. Chronopoulos, and C. Tzanis (2001), Aircraft observations of the solar ultraviolet irradiance throughout the troposphere, J. Geophys. Res. 106, D14, 14843-14854.
• Viotti, P., G. Liuti, and P. Di Genova (2002), Atmospheric urban pollution: Application of an artificial neural network to the city of Perugia, Ecol. Model. 148, 27-46.
• Wang, W., W. Lu, X. Wang, and A. Leung (2003), Prediction of maximum daily ozone level using combined neural network and statistical characteristics, Environ. Int. 29, 555-562.
• Widrow, B., and M.A. Lehr (1990), 30 years of adaptive neural networks: perceptron, Madaline and backpropagation, Proc. IEEE 78, 9, 1415-1442.
• Wilks, D.S. (2006), Statistical Methods in Atmospheric Sciences, 2nd ed., Elsevier, Amsterdam.
• Willmott, C.J. (1982), Some comments on the evaluation of model performance, Bull. Amer. Meteor. Soc. 63, 1309-1313.
• Willmott, C.J., R.E. Davis, J.J. Feddema, K.M. Klink, D.R. Legates, C.M.A. Rowe, G. Steven, and J. O’Donnell (1985), Statistics for the evaluation and comparison of models, J. Geophys. Res. 90, C5, 8995-9005.
• Wolff, G. (1998), Air pollution. In: R.A. Meyer (ed.), Encyclopedia on Environmental Analysis and Remediation, John Wiley, New York, 129-150.
• Yegnanarayana, B. (2004), Artificial Neural Networks, Prentice Hall, India.
• Yi, J., and V.R. Pybutok (1996), A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area, Environ. Pollut. 92, 3, 349-357.