基于Logistic回归的兴安落叶松林飞火引燃试验

doi:10.11707/j.1001-7488.20210713

Abstract

Abstract:

Objective: Based on the laboratory simulated experimental data of spotting ignition,Logistic model was used to establish a model of factors influencing the spotting fire ignition,and the applicability of Logistic model in predicting spotting ignition was explored,in order to understand the formation mechanism of spotting fire and provide reference to fire behavior forecast. Method: In this study,Larix gmelinii of Daxing'an mountains,Heilongjiang province was targeted. The cones,1 hour and 10 hours time-lag twigs were used as firebrands. Fuel beds with different wind speed,packing ratios and moisture content of combustible substance were constructed for the ignited experiment. Three Logistic models were established for predicting ignited probability of 3 spotting firebrands. Result: A total of 1 800 ignited experiments were conducted for each firebrand,cone 414 times,1 hour time-lag twig 161 times,and 10 hours time-lag twig 337 times,respectively. In the range of experiment designed,wind speed was positively correlated with ignited probability; fuel moisture content was negatively correlated with ignited probability,but the oven-dry weight of fire firebrand had a great influence on ignited probability. When fuel moisture content was 40%,the larger oven-dry weight of 3 firebrands caused the increase of ignition rate. There was no significant correlation between packing ratio and ignited probability. In cone firebrand model,predicting ignition accuracy rate was 87.2%,total predicted accuracy rate was 71.1%. In 1 hour time-lag twig firebrand model,predicting ignition accuracy rate was 79.6%,total predicted accuracy rate was 78.6%. In 10 hour time-lag twig firebrand mode model,predicting ignition accuracy rate was 81.5%,total predicted accuracy rate was 79.5%. Conclusion: The ignition capability of 3 firebrands is in the order of cone,10-hour time lag twig and 1-hour time lag twig. Wind speed and fuel moisture content and firebrand oven-dry weight have significant effect on ignition. All 3 ignited probability Logistic models have high accuracy rate,and they can be used as a reference for the prediction of spotting fire,in order to improve the efficiency of forest fire fighting and reduce casualties.

Key words: Larix gmelinii, spotting fire, firebrand, wind speed, moisture content, Logistic

CLC Number:

S762

Jibin Ning,Daotong Geng,Hongzhou Yu,Xueying Di,Guang Yang. Experiment on Spotting Ignition of Larix gmelinii Forest Based on Logistic Regression[J]. Scientia Silvae Sinicae, 2021, 57(7): 121-130.

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References 0

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试验材料 Experimental materials	长度 Length (cm·ind^-1)	直径 Diameter (cm·ind^-1)	干质量 Dry mass (g· ind^-1)	含水率 Moisture content(%)	灰分 Ash(%)	抽提物 Extract(%)
球果Cone	1.955	2.052	0.566	7.254	0.025	0.083
1 h时滞小枝1 h time-lag twig	5	0.428	0.436	5.649	0.100	3.667
10 h时滞小枝10 h time-lag twig	5	0.956	0.342	2.654	0.130	2.500
可燃物床层Fuel bed	—	—	42.4	29.591	0.031	3.000

变量 Variables	球果模型 Cone model	1 h时滞小枝模型 1 h time-lag twig model	10 h时滞小枝模型 10 h time-lag twig model
温度Temperature	1.256	1.153	1.848
相对湿度 Relative humidity	1.265	2.518	2.167
风速Wind speed	1.141	1.046	1.035
火源干质量 Firebrand ovendry mass	3.571	3.986	5.495
火源含水率 Firebrand moisture content	1.145	1.192	1.159
火源直径 Firebrand diameter	1.572	3.818	5.285
火源长度 Firebrand length	2.960	—	—
可燃物含水率 Fuel moisture content	1.109	1.096	1.114
压缩比Packing ratio	1.103	2.508	1.327

变量 Variables	球果模型 Cone model	1 h时滞小枝模型 1 h time-lag twig model	10 h时滞小枝模型 10 h time-lag twig model
温度Temperature	2	2	0
相对湿度 Relative humidity	2	1	0
风速Wind speed	5	5	5
火源干质量 Firebrand ovendry mass	3	5	0
火源含水率 Firebrand moisture content	0	1	5
火源直径 Firebrand diameter	2	1	1
火源长度 Firebrand length	2	—	—
压缩比 Packing ratio	1	3	0
可燃物含水率 Fuel moisture content	5	5	5

火源类型Firebrand type	变量Variables	系数Coefficient	标准误SE	显著性Sig
球果模型 Cone model	风速Wind speed	1.078	0.083	0.000
	火源干质量Firebrand ovendry weight	2.377	0.397	0.000
	可燃物含水率Fuel moisture content	-3.778	0.579	0.000
	常量Constant	-4.552	0.410	0.000
1 h时滞小枝模型 1 h time-lag twig model	风速Wind speed	1.240	0.135	0.000
	火源干质量Firebrand ovendry weight	3.396	0.560	0.000
	可燃物含水率Fuel moisture content	-2.850	0.849	0.001
	压缩比Packing ratio	-28.679	9.912	0.004
	常量Constant	-4.684	0.887	0.000
10 h时滞小枝模型 10 h time-lag twig model	风速Wind speed	1.575	0.119	0.000
	可燃物含水率Fuel moisture content	-2.427	0.645	0.000
	火源含水率Firebrand moisture content	11.495	4.164	0.006
	常量Constant	-5.919	0.466	0.000

火源类型 Firebrand type	样本类型 Sample type	预测引燃准确率 Prediction accuracy of ignition	预测未燃准确率 Prediction accuracy of unignited	总预测准确率 Total prediction accuracy
球果模型 Cone model	建模样本Modeling sample	87.2	65.9	71.1
球果模型 Cone model	验证样本Validating sample	81.8	65.3	69.3
1 h时滞小枝模型 1 h time-lag twig mode	建模样本Modeling sample	79.6	78.5	78.6
1 h时滞小枝模型 1 h time-lag twig mode	验证样本Validating sample	75.5	81.7	81.2
10 h时滞小枝模型 10 h time-lag twig mode	建模样本Modeling sample	81.5	79.0	79.5
10 h时滞小枝模型 10 h time-lag twig mode	验证样本Validating sample	82.1	77.6	78.7

Experiment on Spotting Ignition of Larix gmelinii Forest Based on Logistic Regression

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