About this document
MEDES2025 WaterLevelForecasting by tuanminhdo1203 is a document available to read on EtoBox.
The document presents a comparative study of deep learning architectures for water level forecasting in the Red River, highlighting the need for accurate forecasting to prevent floods. It identifies a research gap in traditional models and evaluates five deep learning architectures, concluding that Multi-Head Attention and GRU are the most reliable for different datasets. The study emphasizes the importance of input length and suggests future work on hybrid models for improved forecasting accuracy.
- Author
- tuanminhdo1203
- Language
- EN