A Lightning Classification Method Based on Convolutional Encoding Features

Author:

Zhu Shunxing12,Zhang Yang1ORCID,Fan Yanfeng1ORCID,Sun Xiubin2,Zheng Dong1ORCID,Zhang Yijun3ORCID,Lyu Weitao1ORCID,Zhang Huiyi1,Wang Jingxuan1

Affiliation:

1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China

2. College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610200, China

3. Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China

Abstract

At present, for business lightning positioning systems, the classification of lightning discharge types is mostly based on lightning pulse signal features, and there is still a lot of room for improvement. We propose a lightning discharge classification method based on convolutional encoding features. This method utilizes convolutional neural networks to extract encoding features, and uses random forests to classify the extracted encoding features, achieving high accuracy discrimination for various lightning discharge events. Compared with traditional multi-parameter-based methods, the new method proposed in this paper has the ability to identify multiple lightning discharge events and does not require precise detailed feature engineering to extract individual pulse parameters. The accuracy of this method for identifying lightning discharge types in intra-cloud flash (IC), cloud-to-ground flash (CG), and narrow bipolar events (NBEs) is 97%, which is higher than that of multi-parameter methods. Moreover, our method can complete the classification task of lightning signals at a faster speed. Under the same conditions, the new method only requires 28.2 µs to identify one pulse, while deep learning-based methods require 300 µs. This method has faster recognition speed and higher accuracy in identifying multiple discharge types, which can better meet the needs of real-time business positioning.

Funder

2022 China Electric Power Research Institute Laboratory Open Fund Project

S&T Development Fund of CAMS

Basic Research Fund of the Chinese Academy of Meteorological Sciences

National Key Research and Development Program of China

Publisher

MDPI AG

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