文献类型：期刊文献

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

作者：Xiao, Yundan[1] Zhang, Xiongqing[2] Ji, Ping[1]

通信作者：Ji, P[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forestry, Beijing, Peoples R China

年份：2015

卷号：10

期号：3

外文期刊名：PLOS ONE

收录：;Scopus(收录号：2-s2.0-84925583258);WOS:【SCI-EXPANDED(收录号:WOS:000351425400167)】；

基金：The research was sponsored by the National Natural Science Foundation of China and International S&T Cooperation program of China (ISTCP) (No. 2014DFG32140-2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

语种：英文

摘要：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.