High-intensity lightning recognition system using Very Low Frequency signal features

Abstract

Commercial lightning detection networks offer detailed lightning stroke data upon subscription. Alternatively, the lightning monitoring system can be deployed at lower cost using Very Low Frequency (VLF) signal reception. With that, a dual-class high-intensity lightning recognition algorithm was developed using VLF signal features to identify the severity of lightning occurrence in the vicinity of 500 km from a single VLF signal path. The algorithm combined feature extraction, lightning detection, and lightning severity classification modules to identify two lightning severity classes based on the detected lightning peak current (by Lightning Detection System Network). For each lightning stroke sample, 20 VLF signal features (in terms of wavelet coefficient) in five signal parameters of four different levels of frequency bands were extracted as pattern representation prior to the classification. A combination of bagged and boosted trees ensemble classifiers achieved a lightning detection rate of 64.0% and correct lightning severity classification of 60.3%. These outstanding results exceeded lightning detection rate of between 20% and 40% in related past studies that applied the early VLF events technique. Good design practices such as cross-validation, feature selection, and ensemble classifiers contribute good generalisation and unbiased classification for small dataset.