Open AccessAutomated Negative Lightning Return Strokes Classification System
Author(s) -
Faranadia Abdul Haris,
Mohd Zainal Abidin Ab Kadir,
Suhizaz Sudin,
Dalina Johari,
Jasronita Jasni,
S.Z. Mohammad Noor
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2107/1/012022
Subject(s) - lightning (connector) , computer science , waveform , software , graphical user interface , lightning detection , interface (matter) , artificial intelligence , data mining , thunderstorm , physics , meteorology , telecommunications , radar , power (physics) , bubble , quantum mechanics , maximum bubble pressure method , parallel computing , programming language
Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes.