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Lightning severity classification technique using Very Low Frequency signal feature extraction

  • Published:2019-11 Issue: Volume:195 Page:105136
  • ISSN:1364-6826
  • Container-title:Journal of Atmospheric and Solar-Terrestrial Physics
  • language:en
  • Short-container-title:Journal of Atmospheric and Solar-Terrestrial Physics

Author:

Arshad N.S.,Abdullah M.,Samad S.A.,Hatta N.M.

Funder

UKM) research

Ministry of Higher Education

Publisher

Elsevier BV

Subject

Space and Planetary Science,Atmospheric Science,Geophysics

Reference28 articles.

1. Implementation and use of lightning detection network in Malaysia;Abdullah,2008

2. Lightning severity classification utilizing the meteorological parameters: a neural network approach;Azhar Omar,2013

3. Evaluation of support vector machine kernels for detecting network anomalies;Batta,2018

4. Sensitive broadband ELF/VLF radio reception with the AWESOME instrument;Cohen;IEEE Trans. Geosci. Remote Sens.,2010

5. A combined TOA/MDF technology upgrade of the US national lightning detection network;Cummins;J. Geophys. Res. Atmos.,1998

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1. Comparative Study on Lightning Data between the Fengyun-4A Satellite and the National Lightning Monitoring Network in Guizhou, China;Journal of Atmospheric and Solar-Terrestrial Physics;2024-03

2. An application of deep learning for lightning prediction in East Coast Malaysia;e-Prime - Advances in Electrical Engineering, Electronics and Energy;2023-12

3. Feature extraction and application of signal weak shock based on particle swarm optimization neural network development and verification;2022 International Conference on Information System, Computing and Educational Technology (ICISCET);2022-05

4. High-intensity lightning recognition system using Very Low Frequency signal features;Journal of Atmospheric and Solar-Terrestrial Physics;2021-05

5. Lightning prediction using an ensemble learning approach for northeast of Iran;Journal of Atmospheric and Solar-Terrestrial Physics;2020-11
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