[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
A Novel Method for Lightning Prediction by Direct Electric Field Measurements at the Ground Using Recurrent Neural Network
Masamoto FUKAWA, Xiaoqi DENG, Shinya IMAI, Taiga HORIGUCHI, Ryo ONO, Ikumi RACHI, Sihan A, Kazuma SHINOMURA, Shunsuke NIWA, Takeshi KUDO, Hiroyuki ITO, Hitoshi WAKABAYASHI, Yoshihiro MIYAKE, Atsushi HORI
Author information
Keywords: lightning, prediction, LSTM, electric field
JOURNAL FREE ACCESS

2022 Volume E105.D Issue 9 Pages 1624-1628

Details
Download PDF (600K)
Download citation RIS

(compatible with EndNote, Reference Manager, ProCite, RefWorks)

BIB TEX

(compatible with BibDesk, LaTeX)

Text
How to download citation
Contact us
Abstract

A method to predict lightning by machine learning analysis of atmospheric electric fields is proposed for the first time. In this study, we calculated an anomaly score with long short-term memory (LSTM), a recurrent neural network analysis method, using electric field data recorded every second on the ground. The threshold value of the anomaly score was defined, and a lightning alarm at the observation point was issued or canceled. Using this method, it was confirmed that 88.9% of lightning occurred while alarming. These results suggest that a lightning prediction system with an electric field sensor and machine learning can be developed in the future.

Content from these authors
© 2022 The Institute of Electronics, Information and Communication Engineers
Previous article Next article
Favorites & Alerts

Recently viewed articles
Announcements from publisher
  • Readers can also download the PDF from https://search.ieice.org/iss/
  • PayPerView service
  • Please contact trans-d [a] ieice.org, if you want to unlock PDF security.
Share this page
feedback
 Top