Convolutional Long-Short-Term Memory Networks (ConvLSTM) for Weather Prediction using Radar and Satellite Images ⋆
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.
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|