Application research of convolutional neural network and its optimization in lightning electric field waveform recognition
Abstract Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a reco...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85473-6 |
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| author | Caixia Wang Xiaoyi Zhang Hui Yang Jinyuan Guo Jia Xu Zhuling Sun |
| author_facet | Caixia Wang Xiaoyi Zhang Hui Yang Jinyuan Guo Jia Xu Zhuling Sun |
| author_sort | Caixia Wang |
| collection | DOAJ |
| description | Abstract Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a recognition and classification method of pulse signal waveform based on Convolutional Neural Network(CNN) algorithm is designed and implemented. The CNN network model and its parameters were optimized from three aspects: dataset, model parameters, and network structure, achieving a recognition rate of over 90%. The effects of various optimization terms and their different optimization orders on the training time of the model were studied. The results indicate that the CNN algorithm is suitable for the classification and recognition of lightning electric field (LEF) waveforms. Optimization can significantly improve recognition rate. The optimization method of fitting idealized waveforms can reduce noise in the dataset and significantly improve recognition rate, indicating that noise has a significant impact on waveform recognition. Therefore, it is necessary to perform noise preprocessing before recognition. The optimization has a huge impact on training efficiency, increasing training time by about 51% after optimization, but the influence of optimization order on it can be ignored. |
| format | Article |
| id | doaj-art-b08bca7979784048a1a969e9458fa4c7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b08bca7979784048a1a969e9458fa4c72025-01-19T12:21:08ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-85473-6Application research of convolutional neural network and its optimization in lightning electric field waveform recognitionCaixia Wang0Xiaoyi Zhang1Hui Yang2Jinyuan Guo3Jia Xu4Zhuling Sun5School of Applied Science, Beijing Information Science and Technology UniversitySchool of Applied Science, Beijing Information Science and Technology UniversityGuangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology, Southern University of Science and TechnologySchool of Applied Science, Beijing Information Science and Technology UniversitySchool of Applied Science, Beijing Information Science and Technology UniversityInstitute of Atmospheric Physics, LAGEO, Chinese Academy of SciencesAbstract Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a recognition and classification method of pulse signal waveform based on Convolutional Neural Network(CNN) algorithm is designed and implemented. The CNN network model and its parameters were optimized from three aspects: dataset, model parameters, and network structure, achieving a recognition rate of over 90%. The effects of various optimization terms and their different optimization orders on the training time of the model were studied. The results indicate that the CNN algorithm is suitable for the classification and recognition of lightning electric field (LEF) waveforms. Optimization can significantly improve recognition rate. The optimization method of fitting idealized waveforms can reduce noise in the dataset and significantly improve recognition rate, indicating that noise has a significant impact on waveform recognition. Therefore, it is necessary to perform noise preprocessing before recognition. The optimization has a huge impact on training efficiency, increasing training time by about 51% after optimization, but the influence of optimization order on it can be ignored.https://doi.org/10.1038/s41598-025-85473-6Waveform of lightningCNNClassificationRecognitionOptimization |
| spellingShingle | Caixia Wang Xiaoyi Zhang Hui Yang Jinyuan Guo Jia Xu Zhuling Sun Application research of convolutional neural network and its optimization in lightning electric field waveform recognition Scientific Reports Waveform of lightning CNN Classification Recognition Optimization |
| title | Application research of convolutional neural network and its optimization in lightning electric field waveform recognition |
| title_full | Application research of convolutional neural network and its optimization in lightning electric field waveform recognition |
| title_fullStr | Application research of convolutional neural network and its optimization in lightning electric field waveform recognition |
| title_full_unstemmed | Application research of convolutional neural network and its optimization in lightning electric field waveform recognition |
| title_short | Application research of convolutional neural network and its optimization in lightning electric field waveform recognition |
| title_sort | application research of convolutional neural network and its optimization in lightning electric field waveform recognition |
| topic | Waveform of lightning CNN Classification Recognition Optimization |
| url | https://doi.org/10.1038/s41598-025-85473-6 |
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