基于犹豫模糊语言的森林火灾发生风险评估

doi:10.13466/j.cnki.lyzygl.2020.01.024

林业资源管理 ›› 2020, Vol. 0 ›› Issue (1): 183-190.doi: 10.13466/j.cnki.lyzygl.2020.01.024

基于犹豫模糊语言的森林火灾发生风险评估

骆相宇, 李宗敏()

四川大学商学院,成都 610041

收稿日期:2019-11-18 修回日期:2019-12-17 出版日期:2020-02-28 发布日期:2020-05-18
通讯作者: 李宗敏 E-mail:lizongmin@scu.edu.cn
作者简介:骆相宇(1998-),女,四川成都人,学士,从事多属性决策工作。Email: xiangyu_l2019@163.com
基金资助:
国家自然科学基金(71601134)

Risk Assessment of Forest Fire Based on Hesitant Fuzzy Linguistic

Xiangyu LUO, Zongmin LI()

Business School,Sichuan University,Chengdu 610041

Received:2019-11-18 Revised:2019-12-17 Online:2020-02-28 Published:2020-05-18
Contact: Zongmin LI E-mail:lizongmin@scu.edu.cn

摘要/Abstract

摘要：

森林火灾具有分布广、频率高、不确定性强的特点,对森林火灾发生风险进行评估可以为有效防治森林火灾发生、降低损失提供参考。从气象、地形、可燃物3个维度构建森林火灾发生风险的评价指标体系,首次引入犹豫模糊语言描述,提高专家语言评价表达的灵活性。通过犹豫模糊语言混合加权集结算子对语言信息进行集结,得到某区域火灾发生风险的可能性。该方法被应用到大兴安岭森林某片过火地区,对该地区森林火灾发生风险进行评估,证明了该方法的可行性和有效性。

关键词: 森林火灾风险, 犹豫模糊语言, 犹豫模糊语言混合加权算子

Abstract:

Forest fires have the characteristics of wide distribution,high frequency and strong uncertainty.The assessment of forest fire risk can provide references for effective prevention and control of forest fires and reduction of losses.The evaluation index system of forest fire risk is constructed from three dimensions of meteorology,terrain and combustibles.It is the first time to introduce hesitant fuzzy linguistic description,which improves the flexibility of expert language evaluation expression.Through the hesitant fuzzy linguistic hybrid weighed aggregation operator,to aggregate the linguistic information,then get the probability of fire risk in a certain area.This method has been applied to an over fire area of Daxinganling forest to assess the risk of forest fire in this area,and this case proves the feasibility and effectiveness of this method.

Key words: forest fire risk, hesitant fuzzy linguistic, hesitant fuzzy linguistic hybrid weighted aggregation operators

中图分类号:

S726

骆相宇, 李宗敏. 基于犹豫模糊语言的森林火灾发生风险评估[J]. 林业资源管理, 2020, 0(1): 183-190.

Xiangyu LUO, Zongmin LI. Risk Assessment of Forest Fire Based on Hesitant Fuzzy Linguistic[J]. FOREST RESOURCES WANAGEMENT, 2020, 0(1): 183-190.

图/表 2

参考文献 38

[1]	王京歌 . 全球森林消失速度变缓[J]. 生态经济:中文版, 2015,31(11):2-5.
[2]	Zheng Liang, Wang Shengjin, Tian Lu, et al. Query-adaptive late fusion for image search and person re-identification[C]. Boston,MA:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1741-1750.
[3]	王成栋 , 木里县森林火灾过火面积已超20公顷起火原因正在调查中[N]. 四川日报. 2019-05-17(02).
[4]	董兆瑞, 鲍聪颖 . 四川凉山森林火灾30名扑火人员牺牲[N]. 北京日报. 2019-04-02(04).
[5]	宋晓明 . “地球之肺”正面临不能承受之重亚马孙森林大火已持续燃烧16天[EB/OL].( 2019- 08- 22)[2019-11-01].http://baijiahao.baidu.com/s?id=1642520890179913452&wfr=spider&for=pc .
[6]	杜建华, 宫殷婷, 蒋丽伟 . 中国森林火灾发生特征及其与主要气候因子的关系研究[J]. 林业资源管理, 2019(2):7-14.
[7]	苏漳文, 宋禹辉, 郭福涛 , 等. 地形环境因素对塔河林业局人为森林火灾发生的影响[J]. 火灾科学, 2015,24(1):16-25.
[8]	姜伟, 冯仲科, 胡林 . 基于VLBP神经网络的林火风险预测模型[J]. 林业资源管理, 2007(1):95-98.
[9]	Jaafari A, Gholami D M, Zenner E K . A Bayesian modeling of wildfire probability in the Zagros Mountains,Iran[J]. Ecological Informatics, 2017(39):32-44.
[10]	陈岱 . 基于Logistic回归模型的大兴安岭林火预测研究[J]. 林业资源管理, 2019(1):116-122.
[11]	Xie Ying, Peng Minggang . Forest fire forecasting using ensemble learning approaches[J]. Neural Computing and Applications, 2019(31):4541-4550.
[12]	Hashjin S S, Milaghardan A H, Esmaeily A, et al. Forest fire hazard modeling using hybrid AHP and fuzzy AHP methods using MODIS sensor[C]. Munich:2012 IEEE International Geoscience and Remote Sensing Symposium, 2012: 931-934.
[13]	Wang Jian, Ma Weiqing, Song shuting, et al.Fuzzy comprehensive evaluation of forest fire risk on transmission lines based on entropy weight method[C]. Luoyang:Proceedings of the 2013 International Conference on Advanced Mechatronic Systems, 2013: 666-670.
[14]	Tsataltzinos T, Iliadis L S, Spartalis S . Generalized fuzzy-rough set application for forest fire risk estimation feature reduction[J]. Emergency Radiology, 2011,12(6):278-281. doi: 10.1007/s10140-006-0483-y pmid: 16773308
[15]	杨建华, 邵红梅, 兰月新 . 森林火灾风险的灰色模糊综合评价[J]. 内江科技, 2014,35(4):37-38.
[16]	赵迎子, 常玉, 崔洁琼 , 等. 基于多层次模糊综合评判法的小区域森林火灾风险分析[J]. 生物数学学报, 2016,31(2):185-194.
[17]	Torra V, Yasuo N. On Hesitant Fuzzy Sets and Decision[C]. Jeju Island,Korea:2009 IEEE International Conference on Fuzzy Systems, 2009: 1378-1382.
[18]	Rodríguez R, Martínez L, Herrera F . Hesitant fuzzy linguistic term sets[J]. Transactions on Fuzzy System, 2012(20):109-119.
[19]	Kahraman C, Oztaysi B, Onar S C . A multicriteria supplier selection model using hesitant fuzzy linguistic term sets[J]. Journal of Multiple-Valued Logic and Soft Computing, 2016,26(3-5):315-333.
[20]	Xu Hao, Fan Zhiping, Liu Yang , et al. A method for evaluating service quality with hesitant fuzzy linguistic information[J]. International Journal of Fuzzy Systems, 2018,20(5):1523-1538. doi: 10.1007/s40815-018-0476-5
[21]	Liao Huchang, Mi Xiaomei, Yu Qin , et al. Hospital performance evaluation by a hesitant fuzzy linguistic best worst method with inconsistency repairing[J]. Journal of Cleaner Production, 2019(232):657-671.
[22]	王坚强, 吴佳亭 . 基于优序关系的犹豫模糊语言多准则决策方法[J]. 控制与决策, 2015,30(5):887-891.
[23]	彭定洪 . 模糊语言群体多准则决策方法研究[D]. 哈尔滨:哈尔滨理工大学, 2012.
[24]	Yager R R . Families of OWA operators[J]. Fuzzy Sets and Systems, 1993,59(2):125-148. doi: 10.1016/0165-0114(93)90194-M
[25]	徐迎军, 魏翠萍, 李东 . OWA算子赋权新方法及其在房地产价格预测中的应用[J]. 工业技术经济, 2010,29(4):71-74.
[26]	Pyne S J, Andrews P L, Laven R D . Introduction to wildland fire[M]. New York,John Wiley & Sons, 1996: 335-337.
[27]	Ganteaume A, Camia A , et al. A review of the main driving factors of forest fire ignition over Europe[J]. Environmental Management, 2013,51(3):651-662. doi: 10.1007/s00267-012-9961-z
[28]	王伟 . 基于森林火险指数的森林火险区划研究[J]. 林业资源管理, 2015(4):81-85.
[29]	Hong Haoyuan, Xu Chong, Liu Junzhi , et al. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models.The case of Dayu County,China[J]. Science of the Total Environment, 2018,630(2018):1044-1056. doi: 10.1016/j.scitotenv.2018.02.278
[30]	Zaitchik B F, Santanello J A, Kumar S V , et al. Representation of Soil Moisture Feedbacks during Drought in NASA Unified WRF(NU-WRF)[J]. Journal of Hydrometeorology, 2013,14(1):360-367. doi: 10.1175/JHM-D-12-069.1
[31]	Hamadeh N, Hilal A, Daya B, et al. Studying the factors affecting the risk of forest fire occurrence and applying neural networks for prediction[C]. London:Proceedings of 2015 SAI Intelligent Systems Conference, 2015: 522-526.
[32]	Vasilakos C, Kalabokidis K, Hatzopoulos J , et al. Identifying wildland fire ignition factors through sensitivity analysis of a neural network[J]. Natural Hazards, 2009,50(1):125-143. doi: 10.1007/s11069-008-9326-3
[33]	王秋华, 舒立福, 李世友 . 云南主要针叶林可燃物类型划分及特征[J]. 林业资源管理, 2011(2):48-53.
[34]	Bajocco S, Ricotta C.Sofia Bajocco, Carlo Ricotta . Evidence of selective burning in Sardinia(Italy):which land-cover classes do wildfires prefer?[J]. Landscape Ecology, 2008,23(2):241-248. doi: 10.1007/s10980-007-9176-5
[35]	朱教君, 康宏樟, 胡理乐 . 应用全天空照片估计林分透光孔隙度(郁闭度)[J]. 生态学杂志, 2005,24(10):1234-1240.
[36]	Mckenzie D, Raymond C L, Kellogg L K B, et al. Mapping fuels at multiple scales:landscape application of the Fuel Characteristic Classification System[J]. Canadian Journal of Forest Research, 2007,37(12):2421-2437. doi: 10.1139/X07-056
[37]	关克志, 张大军 . 大兴安岭森林火灾对植被影响分析[J]. 环境科学, 1990(5):82-88.
[38]	仪清菊 . 1987 年5月大兴安岭特大火灾气象成因探讨[J]. 气象, 1988(4):31-34.

目标层	子系统层c_i	指标层c_ij
森林火灾发生风险	c₁地形因子	c₁₁ 海拔
		c₁₂ 坡向
		c₁₃ 坡度
		c₁₄坡位
		c₁₄坡位	c₂气象因子	c₂₁ 气温
	c₂₂ 相对湿度
	c₂₃ 降水因子
	c₂₄ 风力
	c₃可燃物因子	c₃₁ 植被种类
		c₃₂ 主要植被树龄
		c₃₃ 植被郁闭度

指标c_i_,_j	评价值s_i_,_j	指标c_i_,_j	评价值s_i_,_j	指标c_i_,_j	评价值s_i_,_j
c_1,1	s₂,s₃,s₄	c_2,1	s₅,s₆,s₇	c_3,1	s₆,s₇
c_1,2	s₅,s₆,s₇	c_2,2	s₆,s₇	c_3,2	s₃,s₄,s₅,s₆
c_1,3	s₄,s₅,s₆	c_2,3	s₅,s₆,s₇	c_3,3	s₅,s₆,s₇
c_1,4	s₄,s₅,s₆,s₇	c_2,4	s₄,s₅,s₆,s₇	-	-

基于犹豫模糊语言的森林火灾发生风险评估

Risk Assessment of Forest Fire Based on Hesitant Fuzzy Linguistic

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