广州市森林火灾危害程度预测研究
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摘要
森林火灾危害程度是指森林火灾发生时,所造成的过火面积、受害林面积、直接和间接经济损失,森林资源的生态价值损失、森林火灾成灾面积占该地区森林总面积的百分比和森林火灾发生的频率程度等各个方面的综合损失。本文首先建立森林火灾危害程度评价指标体系,并通过AHP方法求得各个指标的权重,对收集到的历史数据进行森林火灾危害程度的划分；通过森林火灾危害程度指标值对应的各个影响因子,通过逐步回归分析方法,建立森林火灾危害程度预测模型。主要内容和结论如下：
(1.)通过分析森林火灾所造成的实际危害,此处用于划分森林火灾危害程度的相关因子为一共有六个,分别为森林火灾过火面积和受害森林面积、森林火灾造成单位面积上的直接经济效益损失量、森林火灾造成单位面积上的间接经济效益损失量、森林火灾造成单位面积上的生态价值损失量、森林火灾成灾面积占该地区森林总面积的百分比和森林火灾发生的频丰程度。通过建立森林火灾危害程度评价指标体系,并给出每种具体指标的具体计算方法。
(2)利用AHP方法求出各个指标的权重。首先通过专家调查法首先得出判断矩阵,然后即可求出各个指标的权重。经过专家调查法,得出森林火灾危害程度评价体系各指标的权重分别为：森林火灾过火面积和受害森林面积w1=0.3254；森林火灾造成单位面积上的直接经济效益损失量w2=0.1523；森林火灾造成单位面积上的间接经济效益损失量w3=0..04421；森林火灾造成单位面积上的生态价值损失量心=0.2548；森林火灾成灾面积占该地区森林总面积的百分比w5=0.1615；森林火灾发生的频丰程度w6=0.0618。
(3)根据历史数据划分级别。求出其相对应的森林火灾危害程度指标值。对其进行分级,即：轻灾(0～0.2)、中灾(0.2-0.4)、大灾(0.4-0.6)、重灾(0．6-0.8)、巨灾(0.8～1)。通过计算可知,广州市的森林火灾大多为轻灾,占总数的93.3333%；中灾有3例,占总数的3.33%,大灾、重灾、巨灾分别有1例,分别占总数的1.11%。从划分结果可以看出,广州市的森林火灾大多为轻灾,这主要是由于广州市经济较为发达,并且广州市已经建成了森林防火管理信息系统,能够及时地发现火情并且能够及时地进行扑救,从而使森林火灾的危害程度大大降低。
(4)森林火灾危害程度预测模型的建立。这部分需要建立具体的分析模型,根据影响森林火灾危害程度的主导因子,结合相应的森林火灾危害程度分等级,用逐步回归分析方法,得出森林火灾危害程度预测模型：利用实验数据和理论数据进行精度检验复相关系数R=0.998428,精度较高。并利用未参加建模的数据进行适应性分析,得出其危害程度指标值的有一定偏、差,但预测得到的危害程度准确性为100%。说明此模型对森林火灾危害程度具有较好的预测作用,可被用于实际的预测,并需要在实际的使用过程中逐渐调整。
(5)进行森林火灾危害程度预测研究时,采用历史资料拟合法。使用这种方法时,得到的结果往往出现其计算结果与历史数据有较高的准确性,对未来的预测中相对精确性不太高。这种问题出现的原因可能是由于历史数据的记载过程中出现了较大的偏差,而这种影响程度究竟有多少,还需要进一步进行研究。在.对森林火灾危害程度预测研究时,划分各个评价指标有些指标间是具有一定的联系性的,但其相关性又不是特别强。因此,这种指标间的联系对划分森林火灾危害程度之间是否有影响,还有待进一步研究。
The harms degree of the forest-fire is refers to the forest-fire to occur time, the area of the forest fire, suffers injury to the forest area, loss of direct and indirect economic, loss of forest resources ecology value, The forest-fire disaster area accounts for this local forest total area the percentage and the forest-fire occurs frequency degree and so on each aspect synthesis loss. This article first establishes the forest-fire to harm degree appraisal index system, and obtains each index through the AHP method the weight, to the historical data which collects carries on the forest-fire to harms degree the division;harms degree index value correspondence through the forest-fire each influence factor, through the gradually regression analysis method, establishes the forest-fire to harms degree forecast model. The primary coverage and the conclusion are as follows:
(1) Actual harm creates which through the analysis forest-fire, here uses in dividing the forest-fire to harm degree the multiple coefficient of correlation for altogether to have six, respectively is:the forest-fire goes too far the area and suffers injury the wooded area, the forest-fire creates in the unit area the direct economic efficiency loss quantity,the forest-fire creates in the unit area the indirect economic efficiency loss quantity,the forest-fire creates in the unit area the ecology value loss quantity, the forest-fire disaster area accounts for the frequency abundant degree which this local forest total area the percentage and the forest-fire occurs,Harms degree appraisal index system through the establishment forest-fire, and gives each kind of concrete index the concrete computational method.
(2) Extracts each index using the AHP method the weight. First first obtains the judgment matrix through the expert investigation method, then then extracts each index the weight. Through expert's investigation method, obtains the forest-fire to harm degree appraisal system various indexs the weight respectively is:The forest-fire goes too far the area and suffers injury the wooded area w1= 0.3254; The forest-fire creates in the unit area the direct economic efficiency loss quantity w2=0.1523; The forest-fire creates in the unit area the indirect economic efficiency loss quantity w3=0.0442; The forest-fire creates in the unit area the ecology value loss quantity w4= 0.2548; The forest-fire disaster area accounts for this local forest total area the percentage w5=0.1615; The forest-fire occurs frequency abundant degree w6=0.0618.
(3) According to historical data division rank.Extracts the forest-fire which its corresponds to harm degree index value. Carries on the graduation to it, I.e.:light disaster (0～0.2)、disaster (0.2～0.4)、big disaster (0.4～0.6)、heavy disaster (0.6-0.8)、great disaster (0.8～1)。May know through the computation; Guangzhou's forest-fire mostly is the light disaster, accounts for the total 93.3333%; the disaster has 3 examples, accounts for the total 3.33%; the big disaster, the heavy disaster, the great disaster have 1 example separately, accounts for the total separately 1.11%. May see from the division result, forest fires of Guangzhou are mostly light calamity, this is mainly because the economy of Guangzhou is comparatively developed, and Guangzhou city build up forest fire prevention the management information system, can find the condition of a fire and can be put out in time in time already, thus make the extent of injury of the forest fire reduce greatly.
(4) The extent of injury of the forest fire predicts the setting-up of the mode.This part needs to set up concrete analysis model, according to influencing the leading factor of the extent of injury of the forest fire, combine the corresponding extent of injury of forest fire to classify, by returning to the analytical method progressively, obtain the extent of injury of the forest fire and predict models: Use the experimental data and theory data to carry on the test of precision and reply the coefficient correlation R=0.998428, the precision is relatively high. And uses has not participated in the modelling the data to carry on the compatible analysis, obtains its harm degree index value to have certain deviation, but forecast obtains the harm degree accuracy is 100%. Explained this model harms the degree to the forest-fire to have the good forecast function, may use in the actual forecast, and needs to adjust gradually in the actual use process.
(5) Carries on when the forest-fire harms the degree forecast research, uses the historical material fitting law. When uses this method, obtains the result often appears its computed result and the historical data has the high accuracy, to future forecast in relative accuracy not too high. This kind of question appears the reason possibly is because in the historical data record process appeared the big deviation, but actually does this kind of influence have how many, but also needs further to conduct the research.When the harms degree forecast research to the forest-fire, divides each appraisal index during some indexs has certain relation, but its relevance is not specially strong. Therefore, whether during this kind of index relation to does divide the forest-fire to harm between the degree influential, but also waits for further studies.
(1.)通过分析森林火灾所造成的实际危害,此处用于划分森林火灾危害程度的相关因子为一共有六个,分别为森林火灾过火面积和受害森林面积、森林火灾造成单位面积上的直接经济效益损失量、森林火灾造成单位面积上的间接经济效益损失量、森林火灾造成单位面积上的生态价值损失量、森林火灾成灾面积占该地区森林总面积的百分比和森林火灾发生的频丰程度。通过建立森林火灾危害程度评价指标体系,并给出每种具体指标的具体计算方法。
(2)利用AHP方法求出各个指标的权重。首先通过专家调查法首先得出判断矩阵,然后即可求出各个指标的权重。经过专家调查法,得出森林火灾危害程度评价体系各指标的权重分别为：森林火灾过火面积和受害森林面积w1=0.3254；森林火灾造成单位面积上的直接经济效益损失量w2=0.1523；森林火灾造成单位面积上的间接经济效益损失量w3=0..04421；森林火灾造成单位面积上的生态价值损失量心=0.2548；森林火灾成灾面积占该地区森林总面积的百分比w5=0.1615；森林火灾发生的频丰程度w6=0.0618。
(3)根据历史数据划分级别。求出其相对应的森林火灾危害程度指标值。对其进行分级,即：轻灾(0～0.2)、中灾(0.2-0.4)、大灾(0.4-0.6)、重灾(0．6-0.8)、巨灾(0.8～1)。通过计算可知,广州市的森林火灾大多为轻灾,占总数的93.3333%；中灾有3例,占总数的3.33%,大灾、重灾、巨灾分别有1例,分别占总数的1.11%。从划分结果可以看出,广州市的森林火灾大多为轻灾,这主要是由于广州市经济较为发达,并且广州市已经建成了森林防火管理信息系统,能够及时地发现火情并且能够及时地进行扑救,从而使森林火灾的危害程度大大降低。
(4)森林火灾危害程度预测模型的建立。这部分需要建立具体的分析模型,根据影响森林火灾危害程度的主导因子,结合相应的森林火灾危害程度分等级,用逐步回归分析方法,得出森林火灾危害程度预测模型：利用实验数据和理论数据进行精度检验复相关系数R=0.998428,精度较高。并利用未参加建模的数据进行适应性分析,得出其危害程度指标值的有一定偏、差,但预测得到的危害程度准确性为100%。说明此模型对森林火灾危害程度具有较好的预测作用,可被用于实际的预测,并需要在实际的使用过程中逐渐调整。
(5)进行森林火灾危害程度预测研究时,采用历史资料拟合法。使用这种方法时,得到的结果往往出现其计算结果与历史数据有较高的准确性,对未来的预测中相对精确性不太高。这种问题出现的原因可能是由于历史数据的记载过程中出现了较大的偏差,而这种影响程度究竟有多少,还需要进一步进行研究。在.对森林火灾危害程度预测研究时,划分各个评价指标有些指标间是具有一定的联系性的,但其相关性又不是特别强。因此,这种指标间的联系对划分森林火灾危害程度之间是否有影响,还有待进一步研究。
The harms degree of the forest-fire is refers to the forest-fire to occur time, the area of the forest fire, suffers injury to the forest area, loss of direct and indirect economic, loss of forest resources ecology value, The forest-fire disaster area accounts for this local forest total area the percentage and the forest-fire occurs frequency degree and so on each aspect synthesis loss. This article first establishes the forest-fire to harm degree appraisal index system, and obtains each index through the AHP method the weight, to the historical data which collects carries on the forest-fire to harms degree the division;harms degree index value correspondence through the forest-fire each influence factor, through the gradually regression analysis method, establishes the forest-fire to harms degree forecast model. The primary coverage and the conclusion are as follows:
(1) Actual harm creates which through the analysis forest-fire, here uses in dividing the forest-fire to harm degree the multiple coefficient of correlation for altogether to have six, respectively is:the forest-fire goes too far the area and suffers injury the wooded area, the forest-fire creates in the unit area the direct economic efficiency loss quantity,the forest-fire creates in the unit area the indirect economic efficiency loss quantity,the forest-fire creates in the unit area the ecology value loss quantity, the forest-fire disaster area accounts for the frequency abundant degree which this local forest total area the percentage and the forest-fire occurs,Harms degree appraisal index system through the establishment forest-fire, and gives each kind of concrete index the concrete computational method.
(2) Extracts each index using the AHP method the weight. First first obtains the judgment matrix through the expert investigation method, then then extracts each index the weight. Through expert's investigation method, obtains the forest-fire to harm degree appraisal system various indexs the weight respectively is:The forest-fire goes too far the area and suffers injury the wooded area w1= 0.3254; The forest-fire creates in the unit area the direct economic efficiency loss quantity w2=0.1523; The forest-fire creates in the unit area the indirect economic efficiency loss quantity w3=0.0442; The forest-fire creates in the unit area the ecology value loss quantity w4= 0.2548; The forest-fire disaster area accounts for this local forest total area the percentage w5=0.1615; The forest-fire occurs frequency abundant degree w6=0.0618.
(3) According to historical data division rank.Extracts the forest-fire which its corresponds to harm degree index value. Carries on the graduation to it, I.e.:light disaster (0～0.2)、disaster (0.2～0.4)、big disaster (0.4～0.6)、heavy disaster (0.6-0.8)、great disaster (0.8～1)。May know through the computation; Guangzhou's forest-fire mostly is the light disaster, accounts for the total 93.3333%; the disaster has 3 examples, accounts for the total 3.33%; the big disaster, the heavy disaster, the great disaster have 1 example separately, accounts for the total separately 1.11%. May see from the division result, forest fires of Guangzhou are mostly light calamity, this is mainly because the economy of Guangzhou is comparatively developed, and Guangzhou city build up forest fire prevention the management information system, can find the condition of a fire and can be put out in time in time already, thus make the extent of injury of the forest fire reduce greatly.
(4) The extent of injury of the forest fire predicts the setting-up of the mode.This part needs to set up concrete analysis model, according to influencing the leading factor of the extent of injury of the forest fire, combine the corresponding extent of injury of forest fire to classify, by returning to the analytical method progressively, obtain the extent of injury of the forest fire and predict models: Use the experimental data and theory data to carry on the test of precision and reply the coefficient correlation R=0.998428, the precision is relatively high. And uses has not participated in the modelling the data to carry on the compatible analysis, obtains its harm degree index value to have certain deviation, but forecast obtains the harm degree accuracy is 100%. Explained this model harms the degree to the forest-fire to have the good forecast function, may use in the actual forecast, and needs to adjust gradually in the actual use process.
(5) Carries on when the forest-fire harms the degree forecast research, uses the historical material fitting law. When uses this method, obtains the result often appears its computed result and the historical data has the high accuracy, to future forecast in relative accuracy not too high. This kind of question appears the reason possibly is because in the historical data record process appeared the big deviation, but actually does this kind of influence have how many, but also needs further to conduct the research.When the harms degree forecast research to the forest-fire, divides each appraisal index during some indexs has certain relation, but its relevance is not specially strong. Therefore, whether during this kind of index relation to does divide the forest-fire to harm between the degree influential, but also waits for further studies.
引文
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