Bewaard in:
| Hoofdauteur: | |
|---|---|
| Formaat: | Recurso digital |
| Taal: | Engels |
| Gepubliceerd in: |
Zenodo
2026
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| Online toegang: | https://doi.org/10.5281/zenodo.19016825 |
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- <div> <div> <div> <div dir="auto"> <div> <div> <p>This repository presents a deep learning–based framework for short-term prediction of High-Intensity Rainfall (HIR) using GEO-KOMPSAT-2A (GK2A) and Global Precipitation Measurement (GPM) IMERG satellite data. Three ConvLSTM2D (Convolutional Long Short-Term Memory 2D) models were developed utilizing brightness temperature (BT), cloud analysis, and precipitation data. The models achieve up to 50% Critical Success Index (CSI) and over 70% F1 score, demonstrating strong potential for global HIR prediction, particularly in regions where ground-based radar observations are unavailable.</p> </div> </div> </div> </div> </div> </div>