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.

中文翻译：

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一种将机器学习方法分类为云到地和云内闪电的方法

知道雷电放电是否到达地面还是留在雷云内对于雷电安全至关重要，因为云对地雷电对生命和财产构成最大威胁。当前大多数雷电检测网络的分类方法都是基于雷电产生的电磁脉冲的分类方法，但仍有很大的改进空间，其中包括一些需要解决的已知问题。我们提出了一种机器学习方法来对雷电放电进行分类。本研究中使用的分类模型基于支持向量机（SVM）。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，并且还为每次预测提供了概率。