Influence and prediction of climatic factors on forest fires in China

doi:10.16265/j.cnki.issn1003-3033.2023.12.1736

Abstract

Abstract:

In order to explore the influence mechanism of climatic factors on the weight of forest fire accident, firstly, based on the data of forest fire accidents in China from 2011 to 2020, four kinds of climatic factors, including annual average pressure, temperature, precipitation and sunshine time, were selected as characteristic variables. Secondly, the results of forest fire danger zoning were obtained by the hierarchical clustering method, and the grey correlation coefficient was calculated to obtain the influence degree of climate factors on forest fire. Then, the regression model of four types of climatic factors in China was established to obtain the weight ranking of the impact of climatic factors on forest fires, and the risk value in 2021 was predicted. The results show that the fire danger areas in China could be divided into 10 categories under the influence of climatic factors. The influence of climate on the number of forest fires per unit area is ranked as follows: annual average precipitation > annual average pressure > annual average temperature > annual average sunshine time. Linear regression analysis and grey correlation degree analysis have similarities in the ranking results of fire danger zone. The relative error between the predicted risk value by linear regression analysis and the actual value in 2021 is less than 5%.

Key words: climatic factors, forest fire, fire hazard area, hierarchical clustering, correlation, linear regression

LU Yi, ZHOU Qinyun, SHAO Shuzhen, WANG Wei, DAI Yuqing, WEI Xue'e. Influence and prediction of climatic factors on forest fires in China[J]. China Safety Science Journal, 2023, 33(12): 53-59.

Figures/Tables 8

Tab.1

Fig.1

Fig.2

Tab.2

Tab.3

Regression model and test results of four types of climatic factors in China

类别	线性回归模型		相关系数	F值	显著水平
Ⅳ	y=b₁+b₂X₁+b₃X₂+b₄X₃+b₅X₄	(6)	0.671	6.80	0.027
Ⅷ			0.694	8.60	0.025
Ⅸ			0.688	14.99	0.018
Ⅹ			0.879	50.10	0.001
Ⅰ	y=b₁+b₂X₁+b₃X₂+b₄X₃+b₅X₄+b₆ $X 1 2$ +b₇ $X 2 2$ +b₈ $X 3 2$ + b₉ $X 4 2$ +b₁₀X₁X₂+b₁₁X₁X₃+b₁₂X₁X₄+b₁₃X₂X₄+b₁₄X₂X₄+b₁₅X₃X₄	(7)	0.803	6.39	0.001
Ⅱ			0.601	4.53	0.014
Ⅲ			0.868	35.05	0.001
Ⅴ			0.662	3.92	0.001
Ⅵ			0.846	46.40	0.001
Ⅶ			0.634	5.23	0.020

Tab.3

Tab.4

Tab.5

Tab.6

Prediction of forest fire hazards in 2021

省份	年均气压/MPa	年均气温/℃	年均降水量/m	年均日照时间/h	预测值	实际值	误差列
北京	0.100 26	13.00	0.691	2 278.9	0.042 7	0.041 8	0.000 9
天津	0.101 46	13.00	0.609	2 558.4	0.029 0	0.000 0	0.029 0
河北	0.099 34	12.90	0.859	2 160.9	0.013 7	0.013 9	-0.000 2
山西	0.092 82	11.00	0.722	2 458.0	0.021 7	0.021 8	-0.000 1
山东	0.099 01	14.00	0.708	2 249.6	0.007 5	0.007 5	0.000 0
河南	0.095 91	14.00	0.803	1 980.9	0.017 8	0.017 4	0.000 4
陕西	0.093 73	13.00	0.936	1 882.0	0.019 7	0.019 2	0.000 5
内蒙古	0.092 75	3.50	0.328	2 099.6	0.001 2	0.001 1	0.000 1
辽宁	0.103 05	9.30	0.741	2 133.0	0.023 05	0.022 7	0.000 4
吉林	0.098 74	6.80	0.788	2 392.0	0.014 4	0.014 0	0.000 4
黑龙江	0.095 60	4.30	0.804	2 765.0	0.005 0	0.005 0	0.000 0
上海	0.102 07	17.90	1.520	1 740.0	0.050 5	0.000 0	0.050 5
江苏	0.102 56	15.90	1.500	1 799.4	0.012 7	0.012 8	-0.000 1
安徽	0.102 35	18.40	1.290	1 931.5	0.012 2	0.012 6	-0.000 4
湖北	0.097 71	17.50	1.714	1 500.5	0.039 5	0.040 7	-0.001 2
浙江	0.100 86	17.60	1.403	1 384.2	0.058 4	0.057 9	0.000 5
福建	0.100 33	21.20	1.343	1 805.3	0.055 0	0.054 2	0.000 8
江西	0.100 88	18.20	1.310	1 493.1	0.050 9	0.049 0	0.001 9
湖南	0.100 26	18.00	1.342	1 342.5	0.048 8	0.048 5	0.000 3
广西	0.099 45	21.60	1.364	1 669.1	0.073 7	0.072 0	0.001 7
广东	0.100 84	22.90	1.784	1 364.0	0.105 8	0.101 4	0.004 3
海南	0.099 96	25.60	1.613	2 232.3	0.075 7	0.077 1	-0.001 4
重庆	0.096 24	18.20	1.306	1 339.3	0.022 1	0.022 5	-0.000 4
四川	0.089 05	15.40	1.133	1 395.9	0.010 0	0.009 8	0.000 2
贵州	0.094 26	16.70	1.181	1 003.5	0.014 9	0.014 3	0.000 6
云南	0.083 66	17.60	1.157	2 097.7	0.021 1	0.021 8	-0.000 7
西藏	0.065 59	5.40	0.390	2 512.2	0.001 3	0.001 3	0.000 0
甘肃	0.077 27	8.00	0.358	2 353.0	0.011 2	0.011 8	-0.000 6
青海	0.068 86	3.40	0.356	2 170.9	0.012 2	0.011 9	0.000 3
宁夏	0.083 19	8.50	0.296	2 502.0	0.059 3	0.061 0	-0.001 7

Tab.6

References 13

[1]	全国绿化委员会办公室. 2022年中国国土绿化状况公报[J]. 国土绿化, 2023(3): 6-11.
[2]	国家统计局. 2011—2020年中国统计年鉴[M]. 北京: 中国统计出版社, 2012—2021.
[3]	龙腾腾, 殷继艳, 欧朝蓉, 等. 云南省森林火灾风险综合评价及空间格局研究[J]. 中国安全科学学报, 2021, 31(9):167-173. doi: 10.16265/j.cnki.issn1003-3033.2021.09.023
	LONG Tengteng, YIN Jiyan, OU Zhaorong, et al. Comprehensive assessment and spatial pattern study on forest fire risk in Yunnan province[J]. China Safety Science Journal, 2021, 31(9): 167-173. doi: 10.16265/j.cnki.issn1003-3033.2021.09.023
[4]	魏书精, 罗斯生, 罗碧珍, 等. 气候变化背景下森林火灾发生规律研究[J]. 林业与环境科学, 2020, 36(2): 133-143.
	WEI Shujing, LUO Sisheng, LUO Bizhen, et al. Occurrence regularity of forest fire under the background of climate change[J]. Forestry and Environmental Science, 2020, 36(2): 133-143.
[5]	王姊辉, 董恒, 赵洋甬, 等. 应用机器学习模型对中国云贵川区域林火风险预测[J]. 东北林业大学学报, 2023, 51(5): 113-119.
	WANG Zihui, DONG Heng, ZHAO Yangyong, et al. Prediction of forest fire risk in Yunnan, Guizhou and Sichuan using machine learning model[J]. Journal of Northeast Forestry University, 2023, 51(5): 113-119.
[6]	李思宇, 梁达, 韦燕芳, 等. 基于贝叶斯网络的干旱-森林火灾灾害链定量建模研究[J]. 自然灾害学报, 2023, 32(1): 38-46.
	LI Siyu, LIANG Da, WEI Yanfang, et al. Quantitative modeling of drought-forest fire disaster chain based on the Bayesian network[J]. Journal of Natural Disasters. 2023, 32(1): 38-46.
[7]	李德, 牛树奎, 龙先华, 等. 四川省森林火灾与气象因子的关系[J]. 西北农林科技大学学报:自然科学版, 2013, 41(6): 67-74.
	LI De, NIU Shukui, LONG Xianhua, et al. Relationship of forest fires and meteorological factors in Sichuan province[J]. Journal of Northwest A&F University: Natural Science Edition, 2013, 41(6): 67-74.
[8]	李丽琴. 云南省森林火灾发生与气象因子之间的关系研究[D]. 北京: 北京林业大学, 2010.
	LI Liqin. Study on the relationship between forest fires and the meteorological factors in Yunnan[D]. Beijing: Beijing Forestry University, 2010.
[9]	肖云丹, 纪平. 基于贝叶斯-零膨胀负二项模型的森林火灾发生预测研究[J]. 中南林业科技大学学报, 2021, 41(5):49-56.
	XIAO Yundan, JI Ping. Predicting forest fire occurrence based on zero-inflated negative binomial model using Bayesian method[J]. Journal of Central South University of Forestry & Technology, 2021, 41(5):49-56.
[10]	《中国气象年鉴》编辑部. 2011—2020年中国气象年鉴[M]. 北京: 气象出版社, 2012—2021.
[11]	中国气象局. 2021年中国气候公报[R/OL]. (2022-03-08). https://www.cma.gov.cn/zfxxgk/gknr/qxbg/202203/t202203084568477.html.
[12]	肖峻峰, 陈健, 戴程呈, 等. 高含硫天然气泄漏爆炸与毒性影响因素分析[J]. 中国安全科学学报, 2020, 30(6): 43-49. doi: 10.16265/j.cnki.issn1003-3033.2020.06.007
	XIAO Junfeng, CHEN Jian, DAI Chengcheng, et al. Influencing factors on explosion and toxicity of high-sulfur natural gas[J]. China Safety Science Journal, 2020, 30(6) : 43-49. doi: 10.16265/j.cnki.issn1003-3033.2020.06.007
[13]	解明垒, 马尚权. 基于多元线性回归理论的煤矿瓦斯涌出量预测[J]. 陕西煤炭, 2021, 40(1):26-29, 52.
	XIE Minglei, MA Shangquan. Prediction of coal mine gas emission based on multiple linear regression theory[J]. Shaanxi Coal, 2021, 40(1): 26-29, 52.

类别	省份	聚类距离	气候特征
Ⅰ	重庆、四川、贵州	2~5	季风湿润气候,空气湿润,降水丰沛,太阳辐射弱,日照时间短,冬无严寒,夏无酷暑
Ⅱ	浙江、福建、江西、湖南、广西	4~5	季风明显,全年气温适中,日照较多,雨量充沛,空气湿润
Ⅲ	上海、江苏、安徽、湖北	3~5	亚热带湿润性季风气候,四季分明,日照充分,雨量充沛,冬冷夏热,春温多变
Ⅳ	广东	7	季风气候,热量资源丰富,气候温暖,冬无严寒
Ⅴ	北京、天津、河北、山西、山东、河南、陕西	2~4	温带、亚热带季风气候,夏季高温多雨,冬季寒冷干燥,干旱少雨
Ⅵ	辽宁、吉林、黑龙江	2~3	季风气候,四季分明,寒冷期长;雨量集中,风大,日照丰富
Ⅶ	西藏、甘肃、青海、宁夏、新疆	3~4	晴天多,日照时间长,少雨,干燥,冬寒夏热,夏季短促,气温高,秋季降温快
Ⅷ	内蒙古	6	风大少雨,昼夜温差大,日照时间充足,月降水变化大
Ⅸ	海南	9	四季不分明,夏无酷热,冬无严寒,气温年较差小,年平均气温高
Ⅹ	云南	25	年温差小,日温差大,干湿季节分明

类别	Ⅰ	Ⅱ	Ⅲ	Ⅳ	Ⅴ	Ⅵ	Ⅶ	Ⅷ	Ⅸ	Ⅹ	均值
年均气压系数	0.645	0.675	0.536	0.731	0.711	0.716	0.996	0.571	0.673	0.496	0.675
年均气温系数	0.742	0.694	0.638	0.669	0.751	0.617	0.470	0.718	0.609	0.573	0.648
年均降水量系数	-0.713	-0.665	-0.700	-0.728	-0.732	-0.613	-0.993	-0.752	-0.525	-0.600	-0.702
年均日照时间系数	0.692	0.689	0.606	0.746	0.672	0.595	0.558	0.642	0.654	0.611	0.646

[1]	LIU Dan, LUO Yansheng, LI Shixuan, XU Gangyan, LI Moxiao. Causes and correlation network analysis of civil aviation accidents for whole flight phases [J]. China Safety Science Journal, 2024, 34(3): 84-92.
[2]	WANG Yongjun, ZHENG Qian, ZHANG Hemeng, DONG Wei, ZHANG Xiaoming, SASAKI Kyuro. Correlation analysis between combustion of underground residual coal and surface carbon flux in goaf [J]. China Safety Science Journal, 2024, 34(2): 161-167.
[3]	ZHANG Yutao, GUO Qiang, ZHANG Yuanbo, LI Yaqing, SUN Yali. Correlation analysis and prediction of coal spontaneous combustion risk based on correlation coefficient method [J]. China Safety Science Journal, 2024, 34(1): 125-132.
[4]	LI Xinhong, FU Yaqian, LIU Yazhou, HAN Ziyue, ZHANG Renren. Copula-BN based risk assessment methodology of marine ship collisions [J]. China Safety Science Journal, 2023, 33(9): 204-213.
[5]	WU Ankun, WU Shijun, DING Min, ZHANG Chi, ZHANG Shuxia. Prediction of lightning potential based on LSTM recurrent neural network [J]. China Safety Science Journal, 2023, 33(8): 117-124.
[6]	WANG Qifei, ZHAO Yihan, ZHANG Hui, WANG Jian, WANG Yafei. Evaluation of urban resilience based on PSR model and information sensitivity: a case study of Beijing [J]. China Safety Science Journal, 2023, 33(8): 198-204.
[7]	NIU Lixia, YANG Manyi. Construction of a risk evaluation index system for vaccine supply chain [J]. China Safety Science Journal, 2023, 33(7): 32-38.
[8]	LI Lei, LI Jiang'e, REN Yufei, TANG Xiaoxiao. Research on causes of urban rail delay risk based on HFACS model [J]. China Safety Science Journal, 2023, 33(5): 152-157.
[9]	XU Hu, CHEN Yuhang, HE Jiabin, YAHIA Halabi, LONG Danbing, ZHAO Shixing. Analysis of fall accidents characteristics in building construction based on frequency statistics and χ²-PCC [J]. China Safety Science Journal, 2023, 33(4): 91-99.
[10]	CUI Xin, LU Qingchang, ZHANG Lei, XU Biao, QIN Han, LIU Peng. Research on vulnerability of rail transit network under coordinated attack of associated stations [J]. China Safety Science Journal, 2023, 33(3): 167-173.
[11]	CHEN Shuang, BAI Genming, XIAO Chang, QU Honglue, WANG Lin, LIU Zheyan. Analysis of influence of fault structure on initial ground stress field of high ground stress tunnel [J]. China Safety Science Journal, 2023, 33(2): 146-151.
[12]	PAN Weijun, QIN Liru, LUO Chen, HUANG Yuanjing. Adaptive feature extraction of general aviation forest fire rescue data [J]. China Safety Science Journal, 2023, 33(11): 67-74.
[13]	LI Hua, GUO Haojie, XIE Hui, LIN Peng. Information transmission analysis of forest fire emergency command based on information entropy [J]. China Safety Science Journal, 2023, 33(1): 80-87.
[14]	ZHANG Boxu. Grey correlation analysis of railway accidents [J]. China Safety Science Journal, 2022, 32(S2): 60-63.
[15]	ZHU Xin, LI Jianwei, GUO Wei, BI Sheng, WU Yuefei. Forest fire risk prediction model based on machine learning [J]. China Safety Science Journal, 2022, 32(9): 152-157.