气候变化领域集成服务门户(CCPortal): A Machine-Learning Approach to Classify Cloud-to-Ground and Intracloud Lightning

CCPortal

DOI	10.1029/2020GL091148
	A Machine-Learning Approach to Classify Cloud-to-Ground and Intracloud Lightning
	Zhu Y.; Bitzer P.; Rakov V.; Ding Z.
发表日期	2021
ISSN	0094-8276
卷号	48 期号:1
英文摘要	To know if a lightning discharge reaches the ground or remains within the thundercloud is critical for lightning safety as cloud-to-ground lightning poses the greatest threat to life and property. The current classification methods for most lightning detection networks, which are based on the classification of electromagnetic pulses produced by lightning, still have plenty of room to improve, including some known issues to be addressed. We present a machine-learning approach to classify lightning discharges. The classification model used in this study is based on Support Vector Machines (SVMs). Compared with traditional multiparameter methods, our algorithm does not require extraction of individual pulse parameters and additionally provides a probability for each prediction. Using a representative lightning pulse data collected by the Cordoba Marx Meter Array in Argentina, we found the classification accuracy of our machine-learning algorithm to be 97%, which is higher than that for the existing lightning detection networks. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	Clouds; Electromagnetic pulse; Learning systems; Lightning; Support vector machines; Turing machines; Classification accuracy; Classification methods; Classification models; Cloud-to-ground lightning; Lightning detection; Machine learning approaches; Multi-parameter methods; Support vector machine (SVMs); Learning algorithms
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169267
作者单位	Earth Systems Science Center, University of Alabama in Huntsville, Huntsville, AL, United States; Department of Atmospheric Sciences, University of Alabama in Huntsville, Huntsville, AL, United States; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
推荐引用方式 GB/T 7714	Zhu Y.,Bitzer P.,Rakov V.,et al. A Machine-Learning Approach to Classify Cloud-to-Ground and Intracloud Lightning[J],2021,48(1).
APA	Zhu Y.,Bitzer P.,Rakov V.,&Ding Z..(2021).A Machine-Learning Approach to Classify Cloud-to-Ground and Intracloud Lightning.Geophysical Research Letters,48(1).
MLA	Zhu Y.,et al."A Machine-Learning Approach to Classify Cloud-to-Ground and Intracloud Lightning".Geophysical Research Letters 48.1(2021).