文献类型：期刊文献

中文题名：黔南地区气象因子与森林火灾发生次数之间的关系

英文题名：Relationship between Fire-Danger Weather and Forest Fire in Qiannan Area

作者：肖云丹[1] 鞠洪波[1] 张雄清[1] 纪平[1]

第一作者：肖云丹

机构：[1]中国林业科学研究院资源信息研究所

年份：2011

卷号：47

期号：10

起止页码：128-133

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金：中国林业科学研究院资源信息研究所科研基金(IFRIT200804)

语种：中文

中文关键词：森林火灾;火险天气;Poisson回归模型;负二项模型;零膨胀负二项模型;Hurdle模型

外文关键词：forest fire; fire-danger weather; Poisson regression model; negative binomial model; zero-inflated negative binomial model; Hurdle model

分类号：S762.2

摘要：对黔南区春季防火期森林火灾数据进行分析,分别引入Poisson回归模型、负二项模型、零膨胀负二项模型和Hurdle模型拟合该地区火险天气森林火灾发生数,并对这些模型进行逐步筛选。结果表明:Poisson回归模型不适用于处理过度离散的数据,负二项回归模型相对于Poisson回归模型,比较适用于过离散数据;但是对于零个数过多的数据,这2类模型拟合效果较差,零膨胀负二项模型和Hurdle模型对这类数据有很好的解决办法。零膨胀负二项模型和Hurdle模型拟合效果优于其他2种模型,而且Hurdle模型好于零膨胀负二项模型。
In tbis study, based on data of the forest fire occurrence and meteorological variables in spring fireproofing period in Qiannan area, Poisson regression model, negative binomial model, zero-inflated negative binomial model and Hurdle model were respectively employed to predict the forest fires under fire-danger climate, and those models were eompared with each other based on the prediction. The results showed that： Poisson regression model did not fit well into the over-dispersion data. Negative binomial distribution fitted better into the data than Poisson distribution. But both of them were not suitable for simulating zero drived dispersion data. Zero-inflated negative binomial regression model and Hurdle model were useful methods for sueh data. Zero inflated negative binomial regression model and Hurdle model performed better than other two models in predicting forest fires. Moreover, Hurdle model was even superior to zero- inflated negative binomial model.