Performance Analysis Among Predictive Models of Lightning Occurrence Using Artificial Neural Networks and SMOTE

Authors

Keywords:

ann, Artificial Neural Network, forcasting lightning and smote

Abstract

Lightning represent a potential threat to various society activities, such as damage to telecommunication systems and the distribution of electric power, as well as injury or death of humans beings. Predicting the occurrence of lightning can help in making decisions about the actions that must be taken to minimize the risks of this natural phenomenon. In this study, data from air temperature profiles, dew point temperature and historical lightning data were used to obtain two predictive models of lightning occurrence. The models were obtained by using an artificial neural network. The first model was obtained through unbalanced data and the second one with data balanced with Synthetic Minority Over-sampling Technique (SMOTE). The model performance was tested in five different classes of lightning predictions: ABSENCE, LOW, MODERATE, VERY and SEVERE, considering five prediction periods: case 1 (one hour), case 2 (two hours), case 3 (three hours), case 4 (four hours) and case 5 (five hours). It was observed that the use of the Synthetic Minority Over-sampling Technique improved accuracy in the recognition of atmospheric patterns that lead to the incidence of lightning in the five classes used in the five prediction cases.

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Author Biographies

Elton Alves, Federal University of the South and Southeast of Pará

He Holds a degree in Computer Engineering from Federal University of Pará and Doctor in Electrical Engineering (Energy System) from the Federal University of Pará. Is Adjunct Professor at the Federal University of the South and Southeast of Pará. His areas of interest are: computational intelligence, embedded systems and atmospheric discharges.

Adônis Leal, Federal University of Pará

Received a Master and Doctor degree in Electrical Engineering (Power Systems) from the Federal University of Pará, Belém, Pará, Brazil in 2014 and 2018 respectively. From 2016 to 2017, he worked as a visiting researcher in the Department of Electrical and Computer Engineering at the University of Florida, Gainesville, FL, USA. Since 2018 he is an Adjunct Professor at the Federal University of Pará. His main interests area development of embedded systems, lightning physics, lightning detection and location systems and lightning occurrence in the Amazon.

Márcio Lopes, Management and Operational Center for the Amazon Protection System

Is Graduated in Agronomy and Meteorology, with a Master's degree Environmental Sciences and a Doctor in Electrical Engineering. He is currently a Science & Technology Analyst of the Management and Operational Center for the Amazon Protection System.

Alber Fonseca, Federal University of the South and Southeast of Pará

Is Graduated in Computer Engineering from the Federal University of the South and Southeast of Pará

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Published

2021-06-07

How to Cite

Alves, E., Leal, A., Lopes, M. ., & Fonseca, A. (2021). Performance Analysis Among Predictive Models of Lightning Occurrence Using Artificial Neural Networks and SMOTE. IEEE Latin America Transactions, 19(5), 755–762. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/3969

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