气候变化领域集成服务门户(CCPortal): Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method

CCPortal

DOI	10.3808/jei.202400509
	Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method
	Li, Y. Z.; Wang, Z. L.; Huang, X. Y.
发表日期	2024
ISSN	1726-2135
EISSN	1684-8799
起始页码	43
结束页码	1
卷号	43 期号:1
英文摘要	Driven by climate change, more frequent and extreme wildfires have brought a greater threat to humans globally. Fastspreading wildfires endanger the safety of residents in the wildland-urban interface. To mitigate the hazards of wildfires and facilitate early evacuation, a rapid and accurate forecast of wildfire spread is critical in emergency response. This study proposes a novel dualmodel deep learning approach to achieve a super real-time forecast of 2-dimensional wildfire spread in different scenarios. The first model utilizes the U-Net technique to predict the burnt area up to 5 hours in advance. The second model incorporates ConvLSTM layers to refine the forecasted results based on real-time updated input data. To evaluate the effectiveness of this methodology, we applied it to Sunshine Island, Hong Kong, and generated a numerical database consisting of 210 cases (12,600 samples) to train the deep learning models. The simulated wildfire spread database has a fine resolution of 5 m and a time step of 5 minutes. Results show that both models achieve an overall agreement of over 90% between numerical simulation and AI forecast. The real-time wildfire forecasts by AI only take a few seconds, which is 10(2) similar to 10(4) times faster than direct simulations. Our findings demonstrate the potential of AI in offering fast and high-resolution forecasts of wildfire spread, and the novel contribution is to leverage two models which can work in tandem and be utilized at various stages of wildfire management.
英文关键词	wildfire prediction; artificial intelligence; fire modelling; wildland-urban interface; prescribed burning; smart firefighting
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001158077600004
来源期刊	JOURNAL OF ENVIRONMENTAL INFORMATICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299584
作者单位	Hong Kong Polytechnic University
推荐引用方式 GB/T 7714	Li, Y. Z.,Wang, Z. L.,Huang, X. Y.. Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method[J],2024,43(1).
APA	Li, Y. Z.,Wang, Z. L.,&Huang, X. Y..(2024).Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method.JOURNAL OF ENVIRONMENTAL INFORMATICS,43(1).
MLA	Li, Y. Z.,et al."Super Real-Time Forecast of Wildland Fire Spread by A Dual-Model Deep Learning Method".JOURNAL OF ENVIRONMENTAL INFORMATICS 43.1(2024).