气候变化领域集成服务门户(CCPortal): Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes

CCPortal

DOI	10.1016/j.jenvman.2023.120005
	Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes
	Chen, Rui; He, Binbin; Li, Yanxi; Fan, Chunquan; Yin, Jianpeng; Zhang, Hongguo; Zhang, Yiru
发表日期	2024
ISSN	0301-4797
EISSN	1095-8630
起始页码	351
卷号	351
英文摘要	Accurate estimation of potential wildfire behavior characteristics (PWBC) can improve wildfire danger assessment. However, wildfire behavior has been estimated by most fire spread models with immeasurable uncertainties and difficulties in large-scale applications. In this study, a PWBC estimation model (named PWBC-QR-BiLSTM) was proposed by coupling the Bi-directional Long Short-Term Memory (BiLSTM) and quantile regression (QR) methods. Multi-source data, including fuel, weather, topography, infrastructure, and landscape variables, were input into the PWBC-QR-BiLSTM model to estimate the potential rate of spread (ROS) and fire radiative power (FRP) over western Sichuan of China, and then to estimate the probability density of ROS and FRP. Daily ROS and FRP were extracted from the Global Fire Atlas and the MOD14A1/MYD14A1 product. The optimal PWBC-QR-BiLSTM model was determined using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Results showed that the PWBC-QR-BiLSTM performed well in estimating potential ROS and FRP with high accuracy (ROS: R-2 > 0.7 and MAPE<30%, FRP: R-2 > 0.8 and MAPE<25%). The modal PWBC values extracted from the estimated probability density were closer to the observed values, which can be regarded as a good indicator for wildfire danger assessment. The variable importance analysis also verified that fuel and infrastructure variables played an important role in driving wildfire behavior. This study suggests the potential of utilizing artificial intelligence to estimate PWBC and its probability density to improve the guidance on wildfire management.
英文关键词	Potential wildfire behavior characteristic; Wildfire danger assessment; Probability density; Deep learning; Quantile regression; Bi-directional long short-term memory
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001158714700001
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299435
作者单位	University of Electronic Science & Technology of China
推荐引用方式 GB/T 7714	Chen, Rui,He, Binbin,Li, Yanxi,et al. Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes[J],2024,351.
APA	Chen, Rui.,He, Binbin.,Li, Yanxi.,Fan, Chunquan.,Yin, Jianpeng.,...&Zhang, Yiru.(2024).Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes.JOURNAL OF ENVIRONMENTAL MANAGEMENT,351.
MLA	Chen, Rui,et al."Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes".JOURNAL OF ENVIRONMENTAL MANAGEMENT 351(2024).