Relationship between forest lighting fire occurrence and weather factors in Daxing’an Mountains based on negative binomial model and zero-inflated negative binomial models

doi:10.3773/j.issn.1005-264x.2010.05.011

Abstract

Abstract:

Aims Much research has been carried out on the relationship between forest fire occurrence and weather factors by use of modeling in recent years. However, the data organization used in past research can not satisfy the requirements of models well. Our aims are to determine the regression model that best fits the forest fire data set and provides a new model theory for research on forest fire and its influencing factors in order to forecast lighting fire occurrence.

Methods We used negative binomial (NB) and zero-inflated negative binomial (ZINB) models to describe the relationship between lighting fire occurrence and weather factors in the Daxing’an Mountains for 1980-2005 using SAS 9.1 version and R-Project statistic software and comparing results from these models by use of AIC and Vuong methods.

Important findings Both NB and ZINB models produced results with high significance for each weather factor. Comparison of the two models according to AIC, Vuong and other methods showed that the fitting ability and predictive power of ZINB model are better than those of the NB model. The advantage was also found when we compared the modeling results with Ordinary Least Squares. Then we obtained the best model for the relationship between lighting fire and weather factors.

Key words: forest fire, negative binomial model, weather factor, zero-inflated negative binomial model

GUO Fu-Tao, HU Hai-Qing, JIN Sen, MA Zhi-Hai, ZHANG Yang. Relationship between forest lighting fire occurrence and weather factors in Daxing’an Mountains based on negative binomial model and zero-inflated negative binomial models[J]. Chin J Plant Ecol, 2010, 34(5): 571-577.

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URL: https://www.plant-ecology.com/EN/10.3773/j.issn.1005-264x.2010.05.011

https://www.plant-ecology.com/EN/Y2010/V34/I5/571

Figures/Tables 8

References 16

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参数 Parameter	自由度 df	估值 Estimate	标准误差 Standard error	卡方值 Chi-Square χ²	p
截距 Intercept	1	2.737 9	1.105 4	6.14	0.013 3^*
月平均降水 MAP	1	-0.010 1	0.005 1	3.96	0.046 5^*
月平均温度 MAT	1	0.224 6	0.030 6	54.03	< 0.000 1^***
月相对湿度 MRH	1	-0.090 9	0.020 1	20.41	< 0.000 1^***
离差 Dispersion	1	3.029 4	0.743 3	-	-

参数 Parameter	自由度 df	估值 Estimate	标准误差 Standard error	卡方值 Chi-Square χ²	p
截距 Intercept	1	2.737 9	1.105 4	6.14	0.013 3^*
月平均降水 MAP	1	-0.010 1	0.005 1	3.96	0.046 5^*
月平均温度 MAT	1	0.224 6	0.030 6	54.03	< 0.000 1^***
月相对湿度 MRH	1	-0.090 9	0.020 1	20.41	< 0.000 1^***
离差 Dispersion	1	3.029 4	0.743 3	-	-

参数 Parameter	自由度 df	估值 Estimate	标准误差 Standard error	Z 值 Z value	p
截距 Intercept	1	2.654 4	0.423 6	6.08	< 0.006 1^**
月平均降水 MAP	1	-0.014 6	0.010 0	-4.86	< 0.003 1^**
月平均温度 MAT	1	0.235 8	0.017 4	5.41	0.000 1^***
月相对湿度 MRH	1	-0.067 3	0.004 8	-30.54	< 0.000 1^***

参数 Parameter	自由度 df	估值 Estimate	标准误差 Standard error	Z 值 Z value	p
截距 Intercept	1	2.654 4	0.423 6	6.08	< 0.006 1^**
月平均降水 MAP	1	-0.014 6	0.010 0	-4.86	< 0.003 1^**
月平均温度 MAT	1	0.235 8	0.017 4	5.41	0.000 1^***
月相对湿度 MRH	1	-0.067 3	0.004 8	-30.54	< 0.000 1^***

模型类型 Model types		估值 Estimate	标准误差 Standard error	Z 值 Z value	p
点模型 Count model	截距 Intercept	-3.637 9	0.429 3	-8.47	< 0.000 1^***
	月平均温度 MAT	0.124 6	0.025 3	4.92	< 0.0001^***
	月平均蒸发量 MAE	0.010 4	0.002 8	3.74	0.000 2^***
零膨胀模型 Zero-inflation model	截距 Intercept	-72.175 9	25.676 4	-2.810	0.004 9 ^**
零膨胀模型 Zero-inflation model	月相对湿度 MRH	0.962 9	0.338 9	2.840	0.004 5 ^**