A Machine‐Learning Approach to Classify Cloud‐to‐Ground and Intracloud Lightning
A Machine‐Learning Approach to Classify Cloud‐to‐Ground and Intracloud Lightning
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DOI：
10.1029/2020gl091148
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发表时间：
2020-12
影响因子：
5.2
通讯作者：
Yanan Zhu;P. Bitzer;V. Rakov;Z. Ding
中科院分区：
文献类型：
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作者：
Yanan Zhu;P. Bitzer;V. Rakov;Z. Ding
了解闪电放电是到达地面还是停留在雷云内对于闪电安全至关重要，因为云对地闪电对生命和财产构成最大威胁。目前大多数闪电探测网络的分类方法都是基于闪电产生的电磁脉冲的分类，仍然有很大的改进空间，包括一些已知的问题需要解决。我们提出了一种机器学习方法来对闪电放电进行分类。在这项研究中使用的分类模型是基于支持向量机（SVMs）。与传统的多参数方法相比，我们的算法不需要提取单个脉冲参数，并且还为每个预测提供了概率。使用阿根廷科尔多瓦马克思仪表阵列收集的代表性闪电脉冲数据，我们发现我们的机器学习算法的分类准确率为97%，高于现有闪电检测网络的分类准确率。
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.