森林火灾蔓延模拟及应急路径规划优选

doi:10.11707/j.1001-7488.LYKX20220863

摘要/Abstract

摘要：

目的: 利用云计算手段模拟林火蔓延过程，规划火灾应急路径，以促进救援力量快速、高效到达指定火灾地点，为布置林火阻隔措施预留充足时间。研究也为吉林省通化市东昌区的林火应急响应和预防规划提出建设性意见。方法: 以吉林省通化市东昌区为研究区域进行实地调研和模型推演。着眼于林火点单源和多源差异性，利用应急路径规划模型、Dijkstra算法模拟计算单源林火情况下最短救援路径，并纳入多源林火优先级理论，实现救援资源利用最大化。采用FARSITE模型对区域实例森林火灾进行蔓延速率模拟，获取林火发生后各阶段实时数据，综合考虑森林火点附近资源布局情况以及道路交通能力，进行救援路径的推算和防火道路的布局，以验证方法的可行性，并对森林火灾应急响应救援路径进行智能优选。结果: 在一般森林火灾发生时单个着火点的公式基础上，为2个及以上着火点引入优先级理论，再根据火情严重程度通过Dijkstra算法进行依次推演。此外，增加μ作为通行能力系数，以考虑路况、道路属性对救援通行时间的影响，得到新的路径公式。通过FARSITE模型，对2010年 “5.3”一般森林火灾（20100503-220502-01）进行蔓延推演，在火源点起火5 h后，如果没有得到有效的人为干预，火情将演变为重大森林火灾，并蔓延到人居地区。由于风向影响，火源点东北侧的火情优先级高于北侧，若有条件同时进行救援，应急资源应按优先级倾斜。结论: 模拟结果表明，通过应急路径规划模型、Dijkstra算法和优先级理论，可以提高救援路径的获取速率，提前进行资源调配和人员安排，FARSITE模拟结合防火道路规划，联系火灾发生后各时间段的蔓延情况，能有效提升森林火灾决策效率，事半功倍地形成林火应急响应和预防规划方案。

关键词: 智慧林业, 应急预防规划, 森林火灾决策, 云计算, FARSITE, Dijkstra

Abstract:

Objective: In this study, cloud computing method was used to simulate the spread process of forest fire and plan the fire emergency path, so as to promote the rescue forces to quickly and efficiently reach the designated place, and to reserve enough time for arranging forest fire prevention measures. This study also puts forward constructive suggestions for forest fire emergency response and prevention planning in Dongchang District, Tonghua City, Jilin Province. Methods: An on-site investigation was conducted in Dongchang District, Tonghua City, Jilin Province, and then the model deduction was carried out. With a view to the differences between single-source and multi-source forest fires, the emergency path planning model and Dijkstra algorithm were used to simulate and calculate the shortest rescue path in the case of single-source forest fires, and the multi-source forest fire priority theory was incorporated to maximize the utilization of rescue resources. The FARSITE model was used to simulate the spread rate of forest fires in regional examples, and real-time data of each stage after forest fires occurrence were obtained. In considering the resource layout near forest fires and road traffic capacity comprehensively, the rescue path was calculated and the fire prevention road layout was carried out, to verify the feasibility of the method, and the rescue path of forest fires emergency response was intelligently optimized. Result: Based on the formula of a single fire point in a general forest fire occurrence, the priority theory was introduced for two or more fire points, and then Dijkstra algorithm was used to deduce in turn according to the severity of the fire. In addition, μ, as a capacity coefficient, was added to consider the influence of road conditions and road attributes on the rescue transit time, and obtain a new path formula. By using the FARSITE model, the spread rate of the "5.3" general forest fire (20100503-220502-01) in 2010 was simulated. In five hours after the fire started, if there was no effective human intervention, the fire would turn into a major forest fire and spread to the residential areas. Due to the influence of wind direction, the fire priority on the northeast of the fire source would be higher than that on the north. If there are conditions for simultaneous rescue, the emergency resources should be tilted according to the priority. Conclusion: The simulation results show that by using the emergency path planning model, Dijkstra algorithm and priority theory, the acquisition rate of rescue paths can be improved, and resources and personnel can be allocated in advance. FARSITE simulation combined with fire road planning can effectively improve the efficiency of forest fire decision-making, and form a forest fire emergency response and prevention planning scheme with half the effort.

Key words: intelligent forestry, emergency planning, forest fire decision-making, cloud computing, FARSITE, Dijkstra

中图分类号:

S762

陈卓,刘海洋,黄全义,郑明章,栗健. 森林火灾蔓延模拟及应急路径规划优选[J]. 林业科学, 2024, 60(4): 52-61.

Zhuo Chen,Haiyang Liu,Quanyi Huang,Mingzhang Zheng,Jian Li. Simulation of Forest Fire Spread and Optimization of Forest Fire Emergency Prevention Planning[J]. Scientia Silvae Sinicae, 2024, 60(4): 52-61.

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参考文献 0

	陈　森, 姜　江, 陈英武, 等. 未定路网结构情况下应急物资车辆配送问题模型与应用. 系统工程理论与实践, 2011, 31 (5): 907- 913.
	Chen S, Jiang J, Chen Y W, et al. Emergency logistics distribution problem model under uncertain roadway network structure and its application. Systems Engineering-Theory & Practice, 2011, 31 (5): 907- 913.
	韩大校, 韦　睿, 王晓红, 等. 林火导致树木死亡的作用机制和影响因素的研究进展. 林业科学, 2020, 56 (7): 151- 162.
	Han D X, Wei R, Wang X H, et al. Progress on the mechanisms and influencing factors of tree mortality caused by forest fire: a review. Scientia Silvae Sinicae, 2020, 56 (7): 151- 162.
	方　祥, 王海晖, 陶骏骏, 等. 林火蔓延过程中辐射换热和点燃特性分析. 林业科学, 2019, 55 (3): 97- 105.
	Fang X, Wang H H, Tao J J, et al. Analysis on radiation heat transfer and the ignition associated ignition during forest fire spread. Scientia Silvae Sinicae, 2019, 55 (3): 97- 105.
	葛洪磊, 刘　南. 2014. 复杂灾害情景下应急资源配置的随机规划模型. 系统工程理论与实践, 34(12): 3034−3042.
	Ge H L, Liu N. 2014. A stochastic programming model for relief resources allocation problem based on complex disaster scenarios. Systems Engineering-Theory & Practice, 34(12): 3034−3042. ［in Chinese］
	黄　辉, 吴　翰, 杨佳祺, 等. 基于实时路况信息的灾后应急配送路径选择系统. 系统管理学报, 2018, 27 (1): 50- 56.
	Huang H, Wu H, Yang J, et al. Study on after-disaster emergency relieve supplies transportation route selection system based on real time route information. Journal of Systems & Management, 2018, 27 (1): 50- 56.
	李宗明, 郭　健, 王宇君, 等. 顾及车辆性能的路径规划方法研究. 测绘与空间地理信息, 2020, 43 (11): 100- 105. doi: 10.3969/j.issn.1672-5867.2020.11.028
	Li Z M, Guo J, Wang Y J, et al. Research on path planning method considering vehicle performance. Geomatics & Spatial Information Technology, 2020, 43 (11): 100- 105. doi: 10.3969/j.issn.1672-5867.2020.11.028
	刘志华, 杨　健, 贺红士, 等. 黑龙江大兴安岭呼中林区火烧点格局分析及影响因素. 生态学报, 2011, 31 (6): 1669- 1677.
	Liu Z H, Yang J, He H S, et al. Spatial point analysis of fire occurrence and its influence factor in Huzhong forest area of the Great Xing’an Mountains in Heilongjiang Province, China. Acta Ecologica Sinica, 2011, 31 (6): 1669- 1677.
	罗　东, 秦志光, 赵　洋, 等. 基于GIS的应急物资调运信息系统研究. 计算机应用研究, 2011, 28 (6): 2144- 2146,2150.
	Luo D, Qin Z G, Zhao Y, et al. Research on emergency material transportation information system based on GIS. Application Research of Computers, 2011, 28 (6): 2144- 2146,2150.
	马宏亮, 张建平, 李瑞敏. 基于动态交通分配的应急路径规划. 交通信息与安全, 2012, 30 (4): 85- 88.
	Ma H L, Zhang J P, Li R M. Emergency route planning through dynamic traffic assignment. Journal of Transport Information and Safety, 2012, 30 (4): 85- 88.
	慕安冬, 余世舟. 震后应急期最优路径评估方法. 世界地震工程, 2018, 34 (1): 113- 120.
	Mu A D, Yu S Z. Calculation of optimal path in post-earthquake emergency period. World Earthquake Engineering, 2018, 34 (1): 113- 120.
	那顺陶格陶, 吴国周, 代海燕, 等. 基于FARSITE的森林火蔓延模拟研究. 安徽农业科学, 2013, 41 (19): 8203- 8205.
	Natao Getao, Wu G Z, Dai H Y, et al. Forest fire spread based on FARSITE. Journal of Anhui Agricultural Sciences, 2013, 41 (19): 8203- 8205.
	苏宝莉, 李　宁. Dijkstra算法优化及在GIS系统中求最佳路径的应用. 遥感技术与应用, 2013, 28 (5): 866- 870.
	Su B L, Li N. Dijkstra algorithm and the application of the optimal path in the GIS system. Remote Sensing Technology and Application, 2013, 28 (5): 866- 870.
	唐丽玉, 毛行辉, 陈崇成, 等. 2015. 基于FARSITE的林火蔓延三维可视化模拟. 自然灾害学报, 24(2): 221−227.
	Tang L Y , Mao H H, Chen C C, et al. 2013. Three-dimensional visual simulation of forest fire spread based on FARSITE. Journal of Natural Disasters, 24(2): 221−227. ［in Chinese］
	陶玉柱, 邸雪颖. 2013. 火对森林土壤微生物群落的干扰作用及其机制研究进展. 林业科学, 49(11): 146−157.
	Tao Y Z, Di X Y. 2013. Fire interference on forest soil microbial communities and the mechanism: a review. Scientia Silvae Sinicae, 49(11): 146−157. ［in Chinese］
	王晓红, 王千雪, 于宏影, 等. FARSITE模型原理、结构及其在林火行为模拟中的应用. 温带林业研究, 2022, 5 (4): 55- 60.
	Wang X H, Wang Q X, Yu H Y, et al. Principle and structure of FARSITE model and its application in simulation of forest fire behavior. Journal of Temperate Forestry Research, 2022, 5 (4): 55- 60.
	吴志伟, 贺红士, 胡远满, 等. FARSITE火行为模型的原理、结构及其应用. 生态学杂志, 2012, 31 (2): 494- 500.
	Wu Z W, He H S, Hu Y M, et al. Principle, structure, and application of FARSITE fire behavior model. Chinese Journal of Ecology, 2012, 31 (2): 494- 500.
	肖化顺. 林火管理模糊性术语的界定. 西北林学院学报, 2009, 24 (3): 148- 152.
	Xiao H S. Definition of several fire management terminologies. Journal of Northwest Forestry University, 2009, 24 (3): 148- 152.
	杨　斌, 文洪蕊, 李　峰, 等. 突发情景下应急物资分配决策研究. 安全与环境工程, 2015, 22 (5): 7- 12.
	Yang B, Wen H R, Li F, et al. Distribution decision on emergency materials in unexpected situation. Safety and Environmental Engineering, 2015, 22 (5): 7- 12.
	曾　伟, 刘祥凤, 杨龙光. 森林火险预报研究进展. 中国林副特产, 2013, (1): 82- 84.
	Zeng W, Liu X F, Yang L G. Research progress of forest fire risk forecast. Forest By-Product and Speciality in China, 2013, (1): 82- 84.
	张　杰, 王志勇, 许维胜, 等. 突发事件下应急救援路径选择模型的构建和求解. 计算机应用研究, 2011, 28 (4): 1311- 1314.
	Zhang J, Wang Z Y, Xu W S, et al. Model and solution of rescue path selection in emergency. Application Research of Computers, 2011, 28 (4): 1311- 1314.
	Allison R S, Johnston J M, Craig G, et al. Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors, 2016, 16 (8): 1310. doi: 10.3390/s16081310
	Baker A G, Catterall C. 2016. Managing fire-dependent vegetation in Byron Shire, Australia: are we restoring the keystone ecological process of fire? Ecological Management & Restoration, 17(1): 47–55.
	Bharany S, Sharma S, Frnda J, et al. Wildfire monitoring based on energy efficient clustering approach for FANETS. Drones, 2022, 6 (8): 193. doi: 10.3390/drones6080193
	Cao W G, Huang X C, Shu F L. Unmanned rescue vehicle navigation with fused DQN algorithm proceedings of the 2019 international conference on robotics, Intelligent Control and Artificial Intelligence. Shanghai China. ACM, 2019, 6, 556- 561.
	Chen J H, Yang Y, Peng L, et al. Knowledge graph representation learning-based forest fire prediction. Remote Sensing, 2022a, 14 (17): 4391. doi: 10.3390/rs14174391
	Chen J H , Zeng S Q, Xu X L. 2022b. Environment-aware path planning for UAV-assisted search and rescue2022 14th international conference on wireless communications and signal processing (WCSP), November 1−3, 2022. Nanjing, China. IEEE, 922−927.
	Chen Q, Xu N. 2019. Research on the shortest path analysis method in complex traffic environment based on GIS2019 IEEE 4th advanced information technology, Electronic and Automation Control Conference (IAEAC), December 20−22, 2019. Chengdu, China. IEEE. 208−212.
	Cheng X Z, Zhao X Z, Xiao S, et al. 2022. Research on real-time rescue path planning after earthquake based on priority queue. Proc SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021): 925−932.
	Ciesielski M, Balazy R, Borkowski B, et al. Contribution of anthropogenic, vegetation, and topographic features to forest fire occurrence in Poland. IForest - Biogeosciences and Forestry, 2022, 15 (4): 307- 314. doi: 10.3832/ifor4052-015
	Costafreda-Aumedes S, Comas C, Vega-Garcia C. Human-caused fire occurrence modelling in perspective: a review. International Journal of Wildland Fire, 2017, 26 (12): 983. doi: 10.1071/WF17026
	Gosiewski Z, Kwaśniewski K. 2020. Time minimization of rescue action realized by an autonomous vehicle. Electronics, 9(12): 2099.
	Guo F, Zhang L, Jin S, et al. Modeling anthropogenic fire occurrence in the boreal forest of China usinglogistic regression and random forests. Forests, 2016, 7 (11): 250.
	Guo J, Hou R, Zhou M, et al. Monitoring 2019 forest fires in southeastern Australia with GNSS technique. Remote Sensing, 2021, 13 (3): 386. doi: 10.3390/rs13030386
	Hoekstra A, Buurman J, van Ginkel K C H. Urban water security: a review. Environmental Research Letters, 2018, 13 (5): 053002. doi: 10.1088/1748-9326/aaba52
	Kale M P, Mishra A, Pardeshi S, et al. Forecasting wildfires in major forest types of India. Frontiers in Forests and Global Change, 2022, 5, 882685. doi: 10.3389/ffgc.2022.882685
	Khan S, Khan A. FFireNet: Deep learning based forest fire classification and detection in smart cities. Symmetry, 2022, 14 (10): 2155. doi: 10.3390/sym14102155
	Kumar S, Kumar A. Hotspot and trend analysis of forest fires and its relation to climatic factors in the western Himalayas. Natural Hazards, 2022, 114 (3): 3529- 3544. doi: 10.1007/s11069-022-05530-5
	Kurbanov E, Vorobev O, Lezhnin S, et al. Remote sensing of forest burnt area, burn severity, and post-fire recovery: a review. Remote Sensing, 2022, 14 (19): 4714. doi: 10.3390/rs14194714
	Laschi A, Foderi C, Fabiano F, et al. Forest road planning, construction and maintenance to improve forest fire fighting: a review. Croatian Journal of Forest Engineering:Journal for Theory and Application of Forestry Engineering, 2019, 40 (1): 207- 219.
	Li W H, Xu Q L, Yi J H, et al. Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China. Scientific Reports, 2022a, 12, 19029. doi: 10.1038/s41598-022-23697-6
	Li X L, Vázquez-López A, Sánchez del Río Sáez J, et al. Recent advances on early-stage fire-warning systems: mechanism, performance, and perspective. Nano-Micro Letters, 2022b, 14 (1): 197. doi: 10.1007/s40820-022-00938-x
	Miezïte L E, Ameztegui A, De Cáceres M, et al. Trajectories of wildfire behavior under climate change. Can forest management mitigate the increasing hazard? Journal of Environmental Management, 2022, 322, 116134.
	Ravinandan M E, Prasad E V, Vijaya Kumar M V. 2016. Adaptive path exploration and cognitive map generation using swarm intelligence. 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT). December 9−10, 2016. Mysuru. IEEE, 318−321.
	Mostafa M, Shataee Jouibary S, Lotfalian M, et al. 2017. Watershed road network analysis with an emphasis on fire fighting management. Journal of Environmental Engineering and Landscape Management, 25(4): 342−353.
	Naderpour M, Rizeei H M, Ramezani F. Forest fire risk prediction: a spatial deep neural network-based framework. Remote Sensing, 2021, 13 (13): 2513. doi: 10.3390/rs13132513
	Nebot À, Mugica F. Forest fire forecasting using fuzzy logic models. Forests, 2021, 12 (8): 1005. doi: 10.3390/f12081005
	Parisien M A, Snetsinger S, Greenberg J A, et al. Spatial variability in wildfire probability across the western United States. International Journal of Wildland Fire, 2012, 21 (4): 313- 327. doi: 10.1071/WF11044
	Piao Y, Lee D K, Park S, et al. 2022. Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms. Geomatics, Natural Hazards and Risk, 13(1): 2649−2673.
	Picchio R, Pignatti G, Marchi E, et al. The application of two approaches using GIS technology implementation in forest road network planning in an Italian Mountain Setting. Forests, 2018, 9 (5): 277. doi: 10.3390/f9050277
	Purfürst T. The permanent German forest rescue point system—concept, geographical analysis, and optimisation. Forests, 2022, 13 (2): 197. doi: 10.3390/f13020197
	Shen L, Wang F R, Hu L, et al. Rescue vehicle allocation problem based on optimal reliable path under uncertainty. Journal of Central South University, 2022, 29 (11): 3779- 3792. doi: 10.1007/s11771-022-5188-1
	Shi X L, Gai W M, Xu K. Bi-objective rescue path selection optimization for mine fires based on quantitative risk assessment. Safety Science, 2022, 146, 105492. doi: 10.1016/j.ssci.2021.105492
	Spies T A, Scheller R M, Bolte J P. Adaptation in fire-prone landscapes: interactions of policies, management, wildfire, and social networks in Oregon, USA. Ecology and Society, 2018, 23 (2): 11. doi: 10.5751/ES-10079-230211
	Thompson M P, Gannon B M, Caggiano M D. Forest roads and operational wildfire response planning. Forests, 2021, 12 (2): 110. doi: 10.3390/f12020110
	Thompson M P, Rodríguez y Silva F, Calkin D E, et al. A review of challenges to determining and demonstrating efficiency of large fire management. International Journal of Wildland Fire, 2017, 26 (7): 562- 573. doi: 10.1071/WF16137
	Tian Y P, Wu Z C, Li M Z, et al. Forest fire spread monitoring and vegetation dynamics detection based on multi-source remote sensing images. Remote Sensing, 2022, 14 (18): 4431. doi: 10.3390/rs14184431
	Van Wees D, Van der Werf G R, Randerson J T, et al. The role of fire in global forest loss dynamics. Global Change Biology, 2021, 27 (11): 2377- 2391. doi: 10.1111/gcb.15591
	Wang S, Liu B, Liu W P, et al. 2021. Research on the best route strategy model of rescue patrol based on Floyd algorithm2021 7th international conference on hydraulic and civil engineering & smart water conservancy and intelligent disaster reduction forum (ICHCE & SWIDR), November 6-8, 2021. Nanjing, China. IEEE, 48−51.
	Wang Z Y, Zlatanova S, Moreno A, et al. A data model for route planning in the case of forest fires. Computers & Geosciences, 2014, 68, 1- 10.
	Wen H Y, Lin Y F, Wu J B. Co-Evolutionary Optimization Algorithm Based on the Future Traffic Environment for Emergency Rescue Path Planning. IEEE Access, 2020, 8, 148125- 148135. doi: 10.1109/ACCESS.2020.3014609
	Wu Z C, Li M Z, Wang B, et al. Analysis of factors related to forest fires in different forest ecosystems in China. Forests, 2022, 13 (7): 1021. doi: 10.3390/f13071021
	Yang L H, Li D M, Tan R P, et al. Particle swarm optimization for the shortest path problem. Journal of Intelligent & Fuzzy Systems, 2021, 41 (1): 1353- 1373.
	Z Rasel Rahman A K, Nabil Sakif S M, Sikder N, et al. Unmanned aerial vehicle assisted forest fire detection using deep convolutional neural network. Intelligent Automation & Soft Computing, 2023, 35 (3): 3259- 3277.
	Zhao M H, Lu J, Hu X J. 2011. A path selection method for traffic accident emergency rescue on expressway network. Proceedings 2011 international conference on transportation, mechanical, and electrical engineering (TMEE), December 16-18, 2011. Changchun, China. IEEE, 1861−1866.
	Zheng S X, Gao P, Zou X J, et al. Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm. Fronters in Plant Science, 2022, 13, 954757. doi: 10.3389/fpls.2022.954757
	Zhou J F, Reniers G. Simulation analysis of the use of emergency resources during the emergency response to a major fire. Journal of Loss Prevention in the Process Industries, 2016, 44, 1- 11. doi: 10.1016/j.jlp.2016.08.007
	Zhu Z, Deng X, Zhao F, et al. How environmental factors affect forest fire occurrence in Yunnan forest region. Forests, 2022, 13 (9): 1392. doi: 10.3390/f13091392

火灾编号Fire No.	起火原因 Fire cause	起火时间 Fire time	发现时间 Discovery time	扑灭时间 Extinguishing time	火场总面积Total fire area/ hm²	受害成林蓄积量Volume of damaged forest/ m³	出动扑火人工 Dispatch fire fighting manpower	扑火经费 Fire fighting funds/ (10⁴ yuan)
20070427-220502-01	农事用火 Farming with fire	2007?04?27 13:15	2007?04?27 13:15	2007?04?27 13:40	0.15	100.00	23	0.23
20080419-220502-01	农事用火 Farming with fire	2008?04?19 14:35	2008?04?19 14:35	2008?04?19 15:00	0.43	0.00	28	0.28
20080422-220502-01	火源不明 Unknown fire source	2008?04?22 02:15	2008?04?22 02:15	2008?04?22 02:40	0.03	0.00	11	0.19
20100503-220502-01	祭祀用火 Sacrifice with fire	2010?05?03 12:10	2010?05?03 12:50	2010?05?03 13:20	1.24	0.00	132	0.55
20100509-220502-01	祭祀用火 Sacrifice with fire	2010?05?09 10:50	2010?05?09 10:55	2010?05?09 11:49	0.26	190.00	76	0.31
20110405-220502-01	祭祀用火 Sacrifice with fire	2011?04?05 13:20	2011?04?05 13:25	2011?04?05 13:50	0.85	0.00	35	0.50
20140331-220502-01	农事用火 Farming with fire	2014?03?31 14:10	2014?03?31 14:20	2014?03?31 15:05	0.60	80.00	45	0.13
20140403-220502-01	野外吸烟 Wild smoking	2014?04?03 15:20	2014?04?03 15:25	2014?04?03 15:57	0.50	0.00	50	0.27
20180430-220502-01	野外吸烟 Wild smoking	2018?04?30 12:50	2018?04?30 13:22	2018?04?30 14:02	0.03	0.00	20	0.26