Affiliations: [a] Graduate Program in Electrical Engineering, Federal University of Pará, Rua Augusto Corrêa, Guamá, Belém, Pará, Brazil
| [b]
Operations and Management Center of the Amazonian Protection System, Avenida Júlio Cesar, Val-de-Cans, Belém, Pará, Brazil
| [c] Center of Natural Sciences and Technology, Pará State University, Travessa Doutor Enéas Pinheiro, Marco, Belém, Pará, Brazil
Correspondence:
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Corresponding author. Elton R. Alves, Graduate Program in Electrical Engineering, Federal University of Pará, Rua Augusto corrêa, Guamá, CEP 66075-110, Belém, Pará, Brazil. Tel.: +55 91 998041834; e-mail: eltonrafaelalves@gmail.com.
Abstract: Atmospheric discharges offer great risks to the population and activities that involve different systems such as telecommunications, energy distribution and transportation. Lightning prediction can contribute to minimize the risks of this natural phenomenon. Therefore the present paper presents a model for lightning prediction based on satellite atmospheric sounding data, calibrated and validated with lightning data in an Amazon region particular area through an investigation that considered five period cases for validation of lightning prediction: case 1 (one hour), case 2 (two hours), case 3 (three hours), case 4 (four hours) and case 5 (five hours). The machine learning technique used to predict lightning was the Artificial Neural Network (ANN) trained with Levenberg-Marquardt backpropagation algorithm to classify modeling related to lightning prediction. This classification relied on the possibility of lightning prediction from the vertical profile of air temperature obtained from satellite NOAA-19. Results show that ANN was capable of identifying adequately the class to which a new event belongs to in relation to categories of occurrence and absence of lightning with better performance than traditional methodologies.
