Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

Pure and Applied Geophysics

Volumn 169, Issue 10, 2012, Pages 1891-1908

  Chattopadhyay, Surajit ^a   Chattopadhyay, Goutami ^b  

^a PAILAN COLLEGE OF MANAGEMENT AND TECHNOLOGY   (India)

^b UNIVERSITY OF CALCUTTA   (India)

Author keywords

Artificial neural network; estimation; meteorological data; prediction; principal component analysis; total ozone

Indexed keywords

ATMOSPHERIC THERMODYNAMICS; ESTIMATION; FORECASTING; MULTILAYERS; NEURAL NETWORKS; OZONE; PRINCIPAL COMPONENT ANALYSIS; RAIN; REGRESSION ANALYSIS;

ARTIFICIAL NEURAL NETWORK MODELS; METEOROLOGICAL DATA; MODELING AND PREDICTIONS; MULTICOLLINEARITY; NEURAL NETWORK MODEL; STATISTICAL ASSESSMENT; TOTAL OZONE; TOTAL OZONE CONCENTRATIONS;

MULTILAYER NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; CLOUD COVER; CONCENTRATION (COMPOSITION); ESTIMATION METHOD; MODELING; MONSOON; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; TOTAL OZONE; URBAN AREA; WINTER;

INDIA;

EID: 84866916636     PISSN: 00334553     EISSN: 14209136     Source Type: Journal    
DOI: 10.1007/s00024-011-0437-5     Document Type: Article

References (83)

1. Abdi, H. (2003), Factor rotations in factor analyses. In: Lewis-Beck M, Bryman A, Futing T, editors. Encyclopedia for research methods for the social sciences. Thousand Oaks (CA), Sage, 792-795.
2. Abdul-Wahab, S. A., Al-Alawi, S. M. (2002), Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks, Environmental Modelling & Software 17, 219-228.
3. Abdul-Wahab, S. A., Al-Alawi, S. M. (2005), Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations, Environmental Modelling and Software, 20, 1263-1271.
4. Ahn, B. S., Cho, S. S., Kim, C. Y. (2000), The integrated methodology of rough set theory and artificial neural network for business failure prediction, Expert Systems with Applications 18, 65-74.
5. Aires, F., Chédin, A., Scott, N. A., Rossow, W. B. (2002), A Regularized Neural Net Approach for Retrieval of Atmospheric and Surface Temperatures with the IASI Instrument, Journal of Applied Meteorology 41, 144-159.
6. Alexandris, D., Varotsos, C., Kondratyev, K. Y., Chronopoulos, G. (1999), On the altitude dependence of solar effective UV, Physics and Chemistry of the Earth Part C-Solar-Terrestial and Planetary Science 24, 515-517.
7. Amari, S., Murata, N., Muller, K-R., Finke, M., Yang, H. H. (1997), Asymptotic Statistical Theory of Overtraining and Cross-Validation, IEEE Transactions on Neural Networks 8, 985-996.
8. Austin, J., Tourpali, K., Rozanov, E., Akiyoshi, H., Bekki, S., Bodeker, G., Brühl, C., Butchart, N., Chipperfield, M., Deushi, M., Fomichev, V. I., Giorgetta, M. A., Gray, L., Kodera, K., Lott, F., Manzini, E., Marsh, D., Matthes, K., Nagashima, T., Shibata, K., Stolarski, R. S., Struthers, H., Tian, W. (2008), Coupled chemistry climate model simulations of the solar cycle in ozone and temperature, Journal of Geophysical Research 113, CiteID D11306, doi: 10. 1029/2007JD009391.
9. Baldwin, M., Dameris, M., Austin, J., Bekki, S., Bregman, B., Butchart, N., Cordero, E., Gillett, N., Graf, H. F., Granier, C., Kinnison, D., Lal, S., Peter, T., Randel, W., Scinocca, J., Shindell, D., Struthers, H., Takahashi, M., Thompsonv, D. (2006), Chapter 5, Scientific assessment of ozone depletion, global ozone research and monitoring project-Report No. 50, World Meteorological Organization, Geneva, Switzerland.
10. Bartlett, P. L. (1998), The Sample Complexity of Pattern Classification With Neural Networks: The Size of the Weights Is More Important Than the Size of the Network, IEEE Transaction on Information Theory, 44, 525-536.
11. Braesicke, P. et al. (2007), A model inter comparison analysing the link between ozone and geopotential height anomalies in January, Atmospheric Chemistry and Physics Discussions 7, 15409-15451.
12. Bourqui, M. S., Taylor, C. P., Shine, K. P. (2005), A new fast stratospheric ozone chemistry scheme in an intermediate general-circulation model II, application to effects of future increases in greenhouse gases, Quarterly Journal of Royal Meteorological Society 131, 2243-2261.
13. Chattopadhyay, S., Chattopadhyay-Bandyopadhyay, G. (2007), Artificial Neural Network versus autoregressive approach: Prediction of total ozone time series, Model Assisted Statistics and Applications 2, 107-120.
14. Chattopadhyay S., Chattopadhyay G. (2010), Univariate modelling of summer-monsoon rainfall time series, Comparison between ARIMA and ARNN, Comptes Rendus Geosciences 342, 100-107.
15. Chattopadhyay, S., Jain, R., Chattopadhyay, G. (2009), Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India, a neurocomputing approach, Meteorological Applications 16, 403-411.
16. Chattopadhyay, S., Bandyopadhyay, G. (2007), Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland, International Journal of Remote Sensing 28, 4471-4482.
17. Chattopadhyay, G. and Chattopadhyay, S. (2009a), Autoregressive forecast of monthly total ozone concentration, A neurocomputing approach, Computers and Geosciences 35, 1925-1932.
18. Chattopadhyay, G. and Chattopadhyay, S. (2009b), Predicting daily total ozone over Kolkata, India, skill assessment of different neural network models, Meteorological Applications 16, 179-190.
19. Comrie, A. C. (1997), Comparing neural networks and regression models for ozone forecasting, Journal of the Air & Waste Management Association 47, 653-663.
20. Corani G. (2005), Air quality prediction in Milan, feed-forward neural networks, pruned neural networks and lazy learning, Ecological Modelling 185, 513-529.
21. Dawson, C. W., Wilby, R. (1998), An artificial neural network approach to rainfall-runoff modeling, Hydrological Sciences Journal 43, 47-66.
22. De Veaux, R. D., Schumi, J., Schweinsberg, J., and Ungar, L. H. (1998), Prediction Intervals for Neural Networks via Nonlinear Regression, Technometrics, 40, 273-282.
23. Duprat, A. F., Huynh, T., Dreyfus, G. (1998), Toward a Principled Methodology for Neural Network Design and Performance Evaluation in QSAR. Application to the Prediction of LogP, J. Chem. Inf. Comput. Sci. 38, 586-594.
24. Dutot, A-L., Rynkiewicz, J., Steiner, F. E., Rude, J. (2007), A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions, Environmental Modelling & Software 22, 1261-1269.
25. Feister, U and Balzer, K. (1991), Surface ozone and meteorological predictors on a subregional scale, Atmospheric Environment. Part A. General Topics 25, 1781-1790.
26. Firor, J. (1990), The Changing Atmosphere, A Global Challenge, Yale University Press, New Haven & London.
27. Gardner, M. W., Dorling, S. R. (2000), Statistical surface ozone models, an improved methodology to account for non-linear behavior, Atmospheric Environment 34, 21-34.
28. Gardner, M. W., Dorling, S. R. (1998), Artificial neural network (multilayer perceptron)-a review of applications in atmospheric sciences, Atmospheric Environment 32, 2627-2636.
29. Ghose, M. K, Paul, R., Banerjee, V. (2005), Assessment of the Status of rban Air pollution And Its Impact on Human Health in the City of Kolkata. Environmental Monitoring and Assessment 108, 151-167.
30. Gómez-Sanchis J., Martín-Guerrero J. D., Soria-Olivas E., Vila-Francés J., Carrasco J. L., del Valle-Tascón, S. (2006), Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration, Atmospheric Environment 40, 6173-6180.
31. Gupta, A. K., Karar, K., Ayoob, S., John, K. (2008), Spatio-temporal characteristics of gaseous and particulate pollutants in an urban region of Kolkata, India. Atmospheric Research 87, 103-115.
32. Haykin, S. S. (2009) Neural networks and learning machines, Prentice Hall.
33. Hsieh, W. W., Tang, T. (1998), Applying Neural Network Models to prediction and data analysis in meteorology and oceanography, Bulletin of the American Meteorological Society 79, 1855-1869.
34. Hudson, R. D., Andrade, M. F., Follette, M. B., Frolov, A. D. (2006), The total ozone field separated into meteorological regimes-Part II: Northern Hemisphere mid-latitude total ozone trends, Atmospheric Chemistry and Physics 6, 5183-5191.
35. Ingrassia, S., Morlini, I. (2005), Neural Network Modeling for Small Datasets, Technometrics, 47, 297-311, doi: 10. 1198/004017005000000058.
36. June, W., Hui-Jun, W. (2010), The Relationship between Total Ozone and Local Climate at Kunming Using Dobson and TOMS Data, Atmospheric and Oceanic Science Letters 3, 207-212.
37. Johansson, E. M., Dowla, F. U., Goodman, D. M. (1991) Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method, International Journal of Neural Systems 2, 291-301.
38. Jolliffe, I. T. (2002), Principal Component Analysis, Second Edition, Springer-Venlag New York, Inc.
39. Karacan, O. C. (2007), Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams, International Journal of Coal Geology 72, 221-239.
40. Karacan, O. C. (2008), Modeling and prediction of ventilation methane emissions of U. S. longwall mines using supervised artificial neural networks, International Journal of Coal Geology 73, 371-387.
41. Kartalopoulos, S. V. (2000), Understanding Neural Networks and Fuzzy Logic- Basic Concepts and Applications; Prentice Hall, New-Delhi, India.
42. Katsambas, A., Varotsos, C. A., Veziryianni, G., Antoniou, C. (1997), Surface solar ultraviolet radiation: A theoretical approach of the SUVR reaching the ground in Athens, Greece. Environmental Science and Pollution Research 4, 69-73.
43. King J. R., Jackson D. A. (1999), Variable selection in large environmental data sets using principal components analysis, Environmetrics 10, 67-77.
44. Kondratyev, K. Y., Varotsos, C. A. (1996), Global total ozone dynamics -Impact on surface solar ultraviolet radiation variability and ecosystems, Environmental Science and Pollution Research 3, 205-209.
45. Lal, S., Chand, L. K, Sahu, L. K., Venkataramani, S., Brasseur, G., Schultz, M. G. (2006), High levels of ozone and related gases over the Bay of Bengal during winter and early spring of 2001. Atmospheric Environment 40, 1633-1644.
46. Lawrence, S., Giles, C. L., and Tsoi, A. C. (1996), What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation, Technical Reports UMIACS-TR-96-22 and CS-TR-3617, Institute for Advanced Computer Studies, University of Maryland.
47. Lawrence, S., Giles, C. L., and Tsoi, A. C. (1997), Lessons in Neural Network Training: Overfitting May Be Harder Than Expected, in Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI-97, pp. 540-545.
48. Londhe, A. L, Padma, Kumari, B., Kulkarni, J. R, Jadhav, D. B. (2005), Monsoon circulation induced variability in total column ozone over India. Current Science 89, 164-167.
49. Lundin, M., Lundin, J., Burke, H. B., Toikkanen, S., Pylkkanen, L., Joensuu, H. (1999), Artificial Neural Networks applied to survival prediction in breast cancer, Oncology 57, 281-286.
50. Maier H. R., Dandy G. C. (2000), Neural networks for the prediction and forecasting of water resources variables, a review of modelling issues and applications, Environmental modeling and Software 15, 101-124.
51. Maier H. R., Dandy G. C. (2001), Neural network based modelling of environmental variables: A systematic approach, Mathematical and Computer Modelling 33, 669-682.
52. Masters, T., 1993. Practical Neural Network Recipes in C++. Academic Press, San Diego, CA.
53. Monks, P. S. (2003), Tropospheric photochemistry. In, Hewitt, C. N., Jackson, A. V. (Eds.), Handbook of Atmospheric Science, Principles and Applications, Blackwell Publishing, Oxford, UK, 156-187.
54. Nguyen, D., Widrow, B. (1990), Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, IJCNN International Joint Conference on Neural Networks, 1990, 21-26, doi: 10. 1109/IJCNN. 1990. 137819.
55. Panchal, G., Ganatra, A., Kosta, Y. P., Panchal, D. (2011), Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden layers, International Journal of Computer Theory and Engineering 3, 332-337.
56. Pires J. C. M., Sousa S.I.V., Pereira M. C., Alvim-Ferraz M. C. M., Martins F. G. (2008) Management of air quality monitoring using principal component and cluster analysis-Part I: SO2 and PM10, Atmospheric Environment 42, 1249-1260.
57. Prybutok, Victor R., Yi, J., Mitchell, D. (2000), Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations, European Journal of Operational Research 122, 31-40.
58. Quintela-del-Río, A., Francisco-Fernández, M. (2011), Analysis of high level ozone concentrations using nonparametric methods, Science of The Total Environment 409, 1123-1133.
59. Randel, W. J., Cobb, J. B. (1994), Coherent variations of monthly mean total ozone and lower stratospheric temperature, Journal of Geophysical Research 99, 5433-5448.
60. Randel, W. J., Wu, F. (1995), TOMS total ozone trends in potential vorticity coordinates, Geophysical Research Letters 22, 683-686.
61. Robeson S. M., Steyn D. G. (1990), Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations, Atmospheric Environment. Part B. Urban Atmosphere 24, 303-312.
62. Rojas, R. (1996), Neural Networks, A Systematic Introduction, Springer-Verlag New York, Inc., New York.
63. Rumelhart D. E., Hinton, G. E., Williams, R. J. (1986), Learning representations by back-propagation errors, Nature 323, 533-536.
64. Sahoo, A., Sarkar, S., Singh, R. P., Kafatos, M., Summers, M. E. (2005), Declining trend of total ozone column over the northern parts of India. International Journal of Remote Sensing 26, 3433-3440.
65. Salazar-Ruiz, E., Ordieres, J. B., Vergara, E. P., Capuz-Rizo, S. F. (2008), Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US), Environmental Modelling & Software 23, 1056-1069.
66. Sarle, W. S. (1994), Neural networks and statistical models. In: Proceedings of the Nineteenth Annual SAS Users Group International Conference, pp. 1538-1550. SAS Institute.
67. Shiva Nagendra, S. M., Khare, M. (2005), Modelling urban air quality using artificial neural network, Clean Technologies and Environmental Policy 7, 116-126.
68. Sousa, S. I. V, Martins, F. G., Alvim-Ferraz, M. C. M., Pereira, M. C. (2007), Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations, Environmental Modelling & Software 22, 97-103.
69. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., Schill, H. (1998), Total ozone series at Arosa (Switzerland): Homogenization and data comparison, Journal of Geophysical Research 103, 5827-5842.
70. Steinbrecht, W., Claude, H., Köhler, U., Hoinka, K. P. (1998), Correlations between tropopause height and total ozone: Implications for long-term changes, Journal of Geophysical Research 103(D15), 183-119, 192, doi: 10. 1029/98JD01929.
71. Tamura, S. I., Tateishi, M. (1997), Capabilities of a four-layered feedforward neural network: Four layers versus three, IEEE Transactions on Neural Networks 8, 251-255.
72. Taylor, J. W., Buizza, R. (2002), Neural Network Load Forecasting with Weather Ensemble Predictions, Power Engineering Review, IEEE 22, doi: 10. 1109/MPER. 2002. 4312413.
73. Thompson, M. L., Reynolds, J., Cox, L. H., Guttorp, P., Sampson, P. D. (2001), A review of statistical methods for the meteorological adjustment of tropospheric ozone, Atmospheric Environment 35, 617-630.
74. Varotsos, C. A., Cracknell, A. P. (1994), 3 Years of Total Ozone Measurements over Athens Obtained Using the Remote-Sensing Technique of a Dobson Spectrophotometer, International Journal of Remote Sensing 15, 1519-1524.
75. Varotsos, C. A., Chronopoulos, G. J., Katsikis, S., Sakellariou, N. K. (1995), Further Evidence Of The Role Of Air-Pollution On Solar Ultraviolet-Radiation Reaching The Ground, International Journal of Remote Sensing 16, 1883-1886.
76. Vaughan, G., Price, J. D. (1991), On the relation between total ozone and meteorology, Quarterly Journal of the Royal Meteorological Society 117, 1281-1298.
77. Wang W., Lu W., Wang X., Leung A. Y. T. (2003), Prediction of maximum daily ozone level using combined neural network and statistical characteristics, Environment International 29, 555-562.
78. Widrow B., Lehr M. A. (1990), 30 years of adaptive neural networks, perceptron, Madaline, and backpropagation, Proceedings of the IEEE 78, 1415-1442.
79. Wilks, D. S. (2006), Statistical methods in atmospheric Sciences, Second Edition, Elsevier Inc.
80. Willmott C. J. (1982), Some Comments on the Evaluation of Model Performance, Bulletin of American Meteorological Society 63, 1309-1313.
81. Yi, J., Prybutok, V. R. (1996), A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area, A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area, Environmental Pollution 92, 349-357.
82. Zhang, M., Ciesielski, V. (1999), Centred Weight Initialization in Neural Networks for Object Detection, In Edwards J. (Ed.) Proceedings of the 22nd Australasian Computer Science Conference Springer, Singapore, pages 39-50.
83. Zhang, G. P. (2003), Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing 50, 159-175.