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Modeling Forest Fire Occurrences Using Count-Data Mixed Models in Qiannan Autonomous Prefecture of Guizhou Province in China

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  • Yundan Xiao
  • Xiongqing Zhang
  • Ping Ji

Abstract

Forest fires can cause catastrophic damage on natural resources. In the meantime, it can also bring serious economic and social impacts. Meteorological factors play a critical role in establishing conditions favorable for a forest fire. Effective prediction of forest fire occurrences could prevent or minimize losses. This paper uses count data models to analyze fire occurrence data which is likely to be dispersed and frequently contain an excess of zero counts (no fire occurrence). Such data have commonly been analyzed using count data models such as a Poisson model, negative binomial model (NB), zero-inflated models, and hurdle models. Data we used in this paper is collected from Qiannan autonomous prefecture of Guizhou province in China. Using the fire occurrence data from January to April (spring fire season) for the years 1996 through 2007, we introduced random effects to the count data models. In this study, the results indicated that the prediction achieved through NB model provided a more compelling and credible inferential basis for fitting actual forest fire occurrence, and mixed-effects model performed better than corresponding fixed-effects model in forest fire forecasting. Besides, among all meteorological factors, we found that relative humidity and wind speed is highly correlated with fire occurrence.

Suggested Citation

  • Yundan Xiao & Xiongqing Zhang & Ping Ji, 2015. "Modeling Forest Fire Occurrences Using Count-Data Mixed Models in Qiannan Autonomous Prefecture of Guizhou Province in China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-12, March.
    • Handle: RePEc:plo:pone00:0120621
    DOI: 10.1371/journal.pone.0120621
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    References listed on IDEAS

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    3. Shonkwiler, John Scott & Shaw, W. Douglass, 1996. "Hurdle Count-Data Models In Recreation Demand Analysis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 21(2), pages 1-10, December.
    4. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
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    1. Volition Tlhalitshi Montshiwa & Ntebogang Dinah Moroke, 2017. "The Effect of Sample Size on the Efficiency of Count Data Models: Application to Marriage Data," Journal of Economics and Behavioral Studies, AMH International, vol. 9(3), pages 6-18.

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