Using Markov Analysis to Study the Impact of Temperature in Bangladesh

Authors

  • Janardan Mahanta University of Chittagong
  • Soumen Kishor Nath University of Chittagong
  • Md. Haronur Rashid University of Chittagong

DOI:

https://doi.org/10.18034/apjee.v6i2.267

Keywords:

Temperature, Markov, Chain

Abstract

In this paper has been studied the temperature trend in Bangladesh. Long-term changes of surface air temperature over Bangladesh have been studied using the available historical data collected by the Bangladesh meteorological Department (BMD). Daily temperature data is collected from BMD in Dhaka and Chittagong. Then month have been divided according to season and their descriptive statistics are computed. Maximum average temperature in pre-monsoon season and minimum average temperature in winter season have been shown in the paper. This study also reveals that temperature has increased over the time. Markov chain analysis has been applied for these data so as to find the stationary probability. After 26 and 13 days stationary probabilities in Dhaka and Chittagong stations respectively have observed.

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Author Biographies

  • Janardan Mahanta, University of Chittagong

    Assistant Professor, Department of Statistics, University of Chittagong, Chittagong, BANGLADESH

  • Soumen Kishor Nath, University of Chittagong

    Assistant Professor, Institute of Education and Research, University of Chittagong, Chittagong, BANGLADESH

  • Md. Haronur Rashid, University of Chittagong

    Research Student, Department of Statistics, University of Chittagong, Chittagong, BANGLADESH

References

Anderson, T. W., & Goodman, L. A. (1957). Statistical inference about Markov chains. The Annals Mathematical Statistics , 89-110. DOI: https://doi.org/10.1214/aoms/1177707039

Chan, J. C. (2006). Comments on "Changes in tropical cyclone number, duration and intensity in a warming environment. Science , 311, 1713. DOI: https://doi.org/10.1126/science.1121522

Feller, W. (1957). An Introduction to Probability Theory and Its Applications, vol.1, 2d ed., John Wiley & Sons, Inc., New York.

Medhi, J. (1984). Stochastic Process. New age international publishers.

Peterson, T. C., X.Zhang, M. B., & Aguirre, J. L. (2008). Changes in North American extremes derived from daily weather data. J. Geophys. Res , 113. DOI: https://doi.org/10.1029/2007JD009453

Rosenberg, S., Vedlitz, A., Cowman, D., & Zahran, S. (2010). Climate change: a profile of U.S. climate scientists perspective. Clim Chang , 101 (3-4), 663-668.

Sahney, S., Benton, M. J., & Ferry, P. (n.d.). "Link between global taxonomic diversity, ecological diversity and the expansion of vertebrates on land". Biology Letters .

Stern, N. (2007). The economics of climate change: The Stern review. Cambridge University Press, Cambridge. DOI: https://doi.org/10.1017/CBO9780511817434

Whittle, P. (1955). Some distribution and moment formulae for the Markov chain. Journal of Royal Statistical Society , 235-242. DOI: https://doi.org/10.1111/j.2517-6161.1955.tb00197.x

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Published

2019-12-31

How to Cite

Mahanta, J. ., Nath, S. K. ., & Rashid, M. H. . (2019). Using Markov Analysis to Study the Impact of Temperature in Bangladesh. Asia Pacific Journal of Energy and Environment, 6(2), 69-76. https://doi.org/10.18034/apjee.v6i2.267

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