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Nícolas de Araújo Moreira
Federal University of Ceara, Fortaleza
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Rubem Vasconcelos Pacelli
Federal University of Ceara, Fortaleza
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Yuri Carvalho Barbosa Silva
Federal University of Ceara, Fortaleza
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Tarcísio Ferreira Maciel
Federal University of Ceara, Fortaleza
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Ingrid Simões
Federal University of Ceara, Fortaleza
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João César Moura Mota
Federal University of Ceara, Fortaleza
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Cerine Hamida
Massachusetts Institute of Technology, Cambridge
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Rodrigo Zambrana Prado
The Weather Force, Toulouse
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Emilie Caillault
Université du Littoral Côte d’Opale, Dunkerque
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Modeste Kacou
Institut de Recherche pour le Développement, Toulouse
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Marielle Gosset
Institut de Recherche pour le Développement, Toulouse
Keywords:
Artificial intelligence, machine learning, ConvLSTM, image processing, weather forecasting
Abstract
Artificial Intelligence techniques, mainly machine and deep learning ones, are becom- ing the most common approach for data prediction. In this context, using these techniques instead of approaches based on classical statistics has shown interesting and important contri- butions to weather prediction. The present paper discusses the prediction of rainfall and clouds direction based on a sequence of 10 and 14 frames of radar images with a loss inferior to 0.06. Two different Convolutional Long-Short Term Memory Networks configurations were tested and this work presents the estimated frames resulting from these algorithms and presents comparisons between them, real data, and with the performance of other works. The results show that these algorithms can be suitable for short-term weather forecasting.