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Artificial Neural Network Model for the Prediction of Thunderstorms over Kolkata

by Litta A. J, Sumam Mary Idicula, C. Naveen Francis
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 11
Year of Publication: 2012
Authors: Litta A. J, Sumam Mary Idicula, C. Naveen Francis
10.5120/7819-1135

Litta A. J, Sumam Mary Idicula, C. Naveen Francis . Artificial Neural Network Model for the Prediction of Thunderstorms over Kolkata. International Journal of Computer Applications. 50, 11 ( July 2012), 50-55. DOI=10.5120/7819-1135

@article{ 10.5120/7819-1135,
author = { Litta A. J, Sumam Mary Idicula, C. Naveen Francis },
title = { Artificial Neural Network Model for the Prediction of Thunderstorms over Kolkata },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 11 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 50-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number11/7819-1135/ },
doi = { 10.5120/7819-1135 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:48:03.994933+05:30
%A Litta A. J
%A Sumam Mary Idicula
%A C. Naveen Francis
%T Artificial Neural Network Model for the Prediction of Thunderstorms over Kolkata
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 11
%P 50-55
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Severe thunderstorms frequently occur over the eastern and north-eastern states of India during the pre-monsoon season (March-May). Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent non-linearity of their dynamics and physics. In this paper, experiments are conducted on artificial neural network (ANN) model to predict severe thunderstorms that occurred over Kolkata on 3 May 2009, 11 May 2009 and 15 May 2009 using thunderstorm affected parameters and validated the model results with observation. The performance of ANN model in predicting hourly surface temperature during thunderstorm days using different learning algorithms are evaluated. A statistical analysis based on mean absolute error, root mean square error, correlation coefficient and percentage of correctness is performed to compare the predicted and observed data. The results show that the ANN model with Levenberg Marquardt algorithm predicted the thunderstorm activities well in terms of sudden fall of temperature and intensity as compared to other learning algorithms.

References
  1. E. N. Lorenz, 1969. "Three approaches to atmospheric predictability," Bulletin of the American Meteorological Society, vol. 50, pp. 345–349.
  2. Doswell, C. A. , III, 1982. "The operational meteorology of convective weather, Vol. I: operational Mesoanalysis," NOAA Technical Memorandum. NSSFC-5. NOAA, National Severe Storms Forecast Center, Kansas City, MO, 135 pp.
  3. D. S. Wilks, 1995. "Statistical methods in the atmospheric sciences, an introduction," International Geophysics Series, vol. 59, Academic Press, New York, 464 pp.
  4. P. B. Mark, D. S. David, W. J. T. David, J. D. Timothy, J. C. Andrew and O. N. Alan, 2003. "Stratospheric memory and skill of extended-range weather forecasts," Science, vol. 301, pp. 636–639.
  5. D. W. A. McCann, 1992. "Neural network short-term forecast of significant thunderstorms," Weather and Forecasting, vol. 7, pp. 525–534.
  6. J. Shao, 1998. "Improving nowcasts of road surface temperature by a back propagation neural network," Weather and Forecasting, vol. 13, pp. 164–171.
  7. M. M. Elkateb, K. Solaiman and Y. Al-Turki, 1998. "A comparative study of medium-weather-dependent load forecasting using enhanced artificial/fuzzy neural network and statistical techniques," Neurocomputing, vol. 23, pp. 3–14.
  8. R. J. Kuligowski and A. P. Barros, 1998. "Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks," Weather and Forecasting, vol. 13, pp. 1194–1205.
  9. I. Maqsood, I. , Khan, M. R. , Abraham, A. , 2000. "Intelligent weather monitoring systems using connectionist models. International Journal of Neural, Parallel and Scientific Computations," vol. 10, pp. 157–178.
  10. S. Chaudhury and S. Chattopadhyay, 2005. "Neuro-Computing based short range prediction of some meteorological parameters during the pre-monsoon season," Soft Computing, pp. 349-354.
  11. M. T. Hagan. H. B. Demuth, M. H. Beale, 1996. "Neural Network Design. " PWS Publishing Company, Boston, Massachusetts.
  12. C. Bishop, 1995. "Neural Networks for Pattern Recognition," University press.
  13. S. Haykin. , 1994. "Neural Networks, A Comprehensive Foundation," New York, Macmillan Publishing.
  14. Mokbnache L. , Boubakeur A. , 2002. ''Comparison of Different Back-Propagation Algorithms used in The Diagnosis of Transformer Oil'' IEEE Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 244-247.
  15. Ki?i Ö. , Uncuoglu E. , 2005. ''Comparaison of Three Back-Propagation Training Algorithms for Two Cases Studies'' Indian Journal of Engineering & Materials Sciences, 12, 434-442.
  16. Esugasini E. , Mashor M. Y. , Isa N. and Othman N. , 2005. ''Performance Comparison for MLP Networks Using Various Back Propagation Algorithms for Breast Cancer Diagnosis'' Lecture Notes in Computer Science, 2005, 3682/2005, 123-130, DOI: 10. 1007/11552451_17
  17. M Mohan Raju, R K Srivastava, Dinesh C S Bisht, H C Sharma and Anil Kumar, 2011. "Development of Artificial Neural- Network-Based Models for the Simulation of Spring Discharge," Advances in Artificial Intelligence Volume 2011, Article ID 686258.
  18. E. D. Übeyli and ?. Güler, 2004. "Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides. " Neurcomputing 62, 349-365.
  19. Johns, R. H. and Doswell, C. A. , 1992. " Severe local storms forecasting. Weather and Forecasting," 7(4), 588-612.
  20. McNulty, R. P. , 1995. "Severe and Convective Weather: A Central Region Forecasting Challenge. " Weather and Forecasting, 10, 187-202.
  21. Asnani, G. C. , 2006. "Tropical Meteorology (Revised Edition). " Vol. II. Sindh Colony: Audh, Pune.
  22. Price, C. , 2006. "Global thunderstorm activity, Sprites. In: M. Fullekrug et al. (eds. ) Sprites," Elves and Intense Lightning Discharges, 85-99.
  23. William, E. R. , 2005. "Lightning and climate: A review," Atmos. Res. , 76, 272-287.
  24. Lopez, L. , Garcia-Ortega and E. , Sanchez, J. L. , 2007. "A short-term forecast model for hail. " Atmospheric Research, 83, 176-184.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial neural networks learning algorithms thunderstorm temperature Levenberg Marquardt

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