Convolutional Long-Short-Term Memory Networks (ConvLSTM) for Weather Prediction using Radar and Satellite Images ⋆ - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Convolutional Long-Short-Term Memory Networks (ConvLSTM) for Weather Prediction using Radar and Satellite Images ⋆

Nicolas De Araújo Moreira
  • Fonction : Auteur
Rubem Vasconcelos
  • Fonction : Auteur
Yuri Carvalho Barbosa Silva
  • Fonction : Auteur
Tarcisio Ferreira Maciel
  • Fonction : Auteur
Ingrid Simoes
  • Fonction : Auteur
João César Moura Mota
  • Fonction : Auteur
Cerine Hamida
  • Fonction : Auteur
Rodrigo Zambrana Prado
  • Fonction : Auteur
Modeste Kacou
Marielle Gosset
  • Fonction : Auteur
  • PersonId : 1183876

Résumé

Artificial Intelligence techniques, mainly machine and deep learning ones, are becoming 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 contributions 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.
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Dates et versions

hal-04079740 , version 1 (24-04-2023)

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  • HAL Id : hal-04079740 , version 1

Citer

Nicolas De Araújo Moreira, Rubem Vasconcelos, Yuri Carvalho Barbosa Silva, Tarcisio Ferreira Maciel, Ingrid Simoes, et al.. Convolutional Long-Short-Term Memory Networks (ConvLSTM) for Weather Prediction using Radar and Satellite Images ⋆. XXIV Congresso Brasileiro de Automática, Oct 2022, Fortaleza - Ceará, Brazil. ⟨hal-04079740⟩
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