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Natural Hazards

, Volume 71, Issue 2, pp 1159–1180 | Cite as

Risk prediction of low temperature in Nanjing city based on grey weighted Markov model

  • Zaiwu Gong
  • Caiqin Chen
  • Xinming Ge
Original Paper

Abstract

In this research, we calculate the days whose temperature is less than or equal to −5 °C in a year according to the data of the daily minimum temperature in Nanjing city (China) from 1951 to 2011, divide these days into 6 categories according to the number (one, two, three, four, five, six and above) of consecutive low-temperature days, and introduce a annual low-temperature weighted index series. We divide the 61 annual low-temperature weighted indexes of 1951–2011 into five states using the mean standard deviation method. Based on the observation sequence of state of low-temperature in 1951–2009, we simulate the real low-temperature states of 2010 and 2011 by employing both weighted Markov method and grey weighted Markov method. The results of both methods show that the values of simulation (predicted) state agree with those of real state. Based on the state series of low temperature in 1951–2011, we predict the future state of low temperature of Nanjing city in 2012 by utilizing both weighted Markov method and grey weighted Markov method. It is found that the risk of low temperature belongs to the third state when we use weighted Markov method; however, the risk of low temperature belongs to the second state when we use the latter method. Although the states of the risk of low temperature are different because they are predicted by different methods, the two states illustrate that the risk of low temperature of Nanjing city in 2012 is in a normal situation.

Keywords

Weighted Markov Risk of low temperature Grey system 

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 71171115, granted from Qinglan Project in Jiangsu Province (China). The authors are grateful to two anonymous referees for their helpful comments on earlier version of this article.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.College of Economics and ManagementNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China

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