Dynamic Data-driven Research on Forest Fire Behavior Overview
DOI:
https://doi.org/10.62051/ijnres.v2n2.06Keywords:
Forest fire behavior, dynamic data-driven.Abstract
In recent years, under the combined effects of climate change and El Niño, extreme weather has been occurring frequently, and natural disasters of all kinds have been increasing. Forest fire is the first of the three major natural disasters in forests, to reduce the possibility of starting forest fires, reduce the damage caused by forest fire disasters, as well as to predict the development trend of forest fires and fight them scientifically, we should systematically and scientifically grasp the law of forest fire occurrence and development. This paper analyses the various influencing factors affecting the behavior of forest fires and classifies meteorological factors and the moisture content of combustible materials as dynamic factors, and topographical factors and other factors of combustible materials except moisture content as slow-change factors. The research on dynamic data-driven forest fire behavior in recent years is reviewed, the advantages of dynamic data-driven technology for simulating fires are analyzed, the constraints of the technology are indicated, and the future development trend is shown.
References
WU Yueyuan,SHU Lifu,WANG Mingyu et al. Overview of forest fires in the world in recent years [J]. Temperate Forestry Research, 2022, 5 (04): 49-54.
Jiao Lianzhi. Mountain fires have become a global problem [J]. Ecological Economy, 2023, 39 (08): 1-4.
YANG Yunyun,TANG Jie,CHEN He et al. Analysis of meteorological conditions of forest fires in Hunan under the background of extreme drought in 2022 - A case study of the "10-17" major forest fire in Xintian, Hunan, in 2022 [J/OL]. Disaster Science, 1-9
Chen J. Concentrated outbreaks of forest fires in many parts of the world [J]. Ecological Economy, 2021, 37 (10): 1-4.
Luo, J. Y. Forest combustion energetics. Harbin: Northeast Forestry University Press, 1992, 61-1221.
WANG Mingyu,LI Tao,REN Yunmao et al. Progress of research on forest fire behavior and special fire behavior [J]. World Forestry Research, 2009, 22 (02): 45-49. DOI:10.13348/j.cnki.sjlyyj.2009.02.007.
YUAN Chunming, WEN Dingyuan. Overview of research on forest fire behavior [J]. World Forestry Research, 2000, (06): 27-31. DOI:10.13348/j.cnki.sjlyyj.2000.06.005.
R.E. Keane, J. L. Garner, et al. Development of the Input Data Layers for the FARSITE Fire Growth Model for the Selway-Bitterroot Wilderness Complex, USA, USDA Forest Service General Technical Report RMRS-GTR-3, 1998: 1-121.
WEI Shu-Jing, HU Hai-Qing, SUN Long. Impact of climate change on forest fire occurrence pattern in China[J]. Forest Fire Prevention,2011,(01):30-34.
Wang Mingyu. Characteristics and trends of forest fire response in China under the background of climate change [D]. China Academy of Forestry Sciences, 2009(01).
HAN Zhigang, TIAN Dalun, ZHANG Gui, LIU Dapeng,. A review of the impact of climate change on the spatial and temporal dynamics of forest fires[J]. Journal of Central South Forestry University of Science and Technology,2010,(05):44-49.
TIAN Changyong, Analysing the influence of meteorological factors on forest fires [J]. Modern rural science and technology,2019,(07):97.
Wang Qiuhua. Research on fire behavior during forest fire combustion [D]. China Academy of Forestry Sciences, 2010.
Zhang Xiaoyu. Impacts of El Niño/La Niña events on forest burnability in the Daxinganling region [D]. Chinese Academy of Forestry Sciences,2019(03).
Liu Jinbo. Research on the prediction model of the water content of combustible material in several forest types in the Kunming area [D]. Northeast Forestry University,2017(05).
Man Ziyuan. Study on the dynamic change of forest combustible moisture content and applicability of humidity code in Nanchang, Jiangxi [D]. Northeast Forestry University.2020(01)
GAO Zhongliang, WANG Hechenyang, WEI Jianheng, CAO Yufei, YU Wentian, WANG Qiuhua, ZHOU Ruliang, HAN Li, WANG Chisel, YU Shoufu. Predictive modeling of surface combustible water content in Yunnan pine forests in central Yunnan [J]. Journal of Ecology,2024,43(02):342-351.
Xu Mingrui. Experimental study on the effect of moisture content on the negative ignition of combustible materials and the transition from negative ignition to open flame [D]. University of Science and Technology of China,2023.
LUO Yongzhong, CHE Kejun, JIANG Zhirong. Study on the change rule of forest combustible water content in Qilian Mountain forest area [Z]. Journal of Gansu Agricultural University,2005(02):239-244.
XU Tan, DONG Snapdragon, ZHAI Hongli. Study on the relationship between surface combustible water content and altitude in forests of the Daxinganling forest area [Z]. Forestry Science and Technology Information, 2023 (02): 32-34.
ZHU Ji-Ping, LIU Si-Ping, LIN Qi-Zhao, WANG Qing-An. An experimental study on some characteristics of fire behavior on the upper slope of a forest surface under variable slope conditions【J】. Fire Science,1999,(02).
WANG Qiuhua, LI Xiaochuan, WANG Zhenshi et al. Research on the influencing factors of forest fire behavior [J]. Inner Mongolia Forestry Survey and Design, 2011, 34 (04): 76-79. DOI:10.13387/j.cnki.nmld.2011.04.028.
SHU Lifu, TIAN Xiaorui, LI Qian, WU Pengchao. Formation of lightning-strike fire and countermeasures for prevention and suppression [J]. Fire Science,1999,(03).
TIAN Zuwei,ZHANG Zhidong,ZENG Ji et al. Overview of the influence of forest vegetation combustible condition on fire behavior [J]. Forest Fire Protection, 2019, (03): 35-37.
Zhang C, Rochoux M, Tang W, et al. Evaluation of data-driven wildland fire spread forecast model with spatially distributed parameter estimation in simulations of the FireFlux I field-scale experiment[J]. Fire Safety Journal, 2017, 91: 758-767.
Benali A ,Sá C A ,Ervilha R A , et al. The fire spread predictions: sweeping uncertainty under the rug [J]. Science of the Total Environment, 2017, 592 187-196.
Méndez-Garabetti M ,Bianchini G ,Caymes-Scutari P , et al. Increase in the quality of the prediction of a computational wildfire behavior method through the improvement of the internal metaheuristic [J]. Fire Safety Journal, 2016, 82 49-62.
Valero M M ,Rios O ,Mata C , et al. An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires [J]. Fire Safety Journal, 2017, 91 835-844.
Cardil A ,Monedero S ,Silva A C , et al. Adjusting the rate of spread of fire simulations in real-time [J]. Ecological Modelling, 2019, 395 39-44.
Rochoux M ,Delmotte B ,Cuenot B , et al. Regional-scale simulations of wildland fire spread informed by real-time flame front observations [J]. Proceedings of the Combustion Institute, 2013, 34 (2): 2641-2647.
Tang Chun. Research on UAV real-time dynamic data-driven optimization method for forest fire spread simulation [D]. Supervisor: Xingdong Li. Northeast Forestry University, 2023.
ZHOU Guoxiong, YIN Kejia, CHEN Aibin. Dynamic data-driven forest fire spread model based on DEVS modeling [J]. Journal of System Simulation,2018,30(10):3642-3647.
Gao Xindan. Research on the theory and method of dynamic data-driven simulation of forest fire spreading [D]. Supervisor: Nihong Wang. Northeast Forestry University, 2012.
Zhou YF. Research on Dynamic Data-Driven Adaptive Simulation Techniques for Forest Fire Spread [D]. Supervisor: Tang Xiaoming. China Academy of Forestry Sciences, 2010.
Qian Lan. Research on the correction effect of ensemble Karman filtering algorithm on FARSITE forest fire spread prediction [D]. University of Science and Technology of China,2019(08).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
