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Detecting long-range correlations in fire sequences with Detrended fluctuation analysis

Author

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  • Zheng, Hongyang
  • Song, Weiguo
  • Satoh, Kohyu

Abstract

The spatial–temporal power-law distributions are found in many natural systems, which have self-similarity and fractal behavior. By analyzing the time series of such systems, we could expect to explore and understand the underlying mechanisms. In this paper, the Detrended fluctuation analysis (DFA) is used to analyze the long-range correlations of forest and urban fires in Japan and China. It is found that the interevent time series of both forest and urban fires have the persistent long-range power-law correlations, and they all have two scaling exponents, α1 and α2, which are both bigger than 0.5 and smaller than 1.0, despite the different regions and countries. For forest fires, 0.61<α1<0.73,0.87<α2<0.98 and for urban fires, 0.52<α1<0.61,0.59<α2<0.88. The result suggests that fires have self-similarity characteristics. The occurrence of forest fires may have connection with the weather fluctuations, which have significant effects on the ignition and have the similar temporal correlations. It is shown that the interval sequences of urban fires closely resemble that of white noise in small timescale, and the correlations are weaker than that of forest fires. Human behavior and human density may affect the long-range correlation in some way. This seems to be helpful to understand the complexity of fire system in temporal aspect.

Suggested Citation

  • Zheng, Hongyang & Song, Weiguo & Satoh, Kohyu, 2010. "Detecting long-range correlations in fire sequences with Detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 837-842.
  • Handle: RePEc:eee:phsmap:v:389:y:2010:i:4:p:837-842
    DOI: 10.1016/j.physa.2009.10.022
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    Cited by:

    1. de Benicio, Rosilda B. & Stošić, Tatijana & de Figueirêdo, P.H. & Stošić, Borko D., 2013. "Multifractal behavior of wild-land and forest fire time series in Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6367-6374.
    2. Shengli, Liu & Yongtu, Liang, 2019. "Exploring the temporal structure of time series data for hazardous liquid pipeline incidents based on complex network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 26(C).
    3. Telesca, Luciano & Song, Weiguo, 2011. "Time-scaling properties of city fires," Chaos, Solitons & Fractals, Elsevier, vol. 44(7), pages 558-568.
    4. Stosic, Tatijana & Stosic, Borko, 2024. "Generalized weighted permutation entropy analysis of satellite hot-pixel time series in Brazilian biomes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).

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