Review of deep learning for hydrological forecasting II：Research progress and prospect
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
Many studies focus on the application of deep learning in data-driven hydrological models for hydrological forecasting. The recent research progress of deep learning models on hydrological forecasting are reviewed. Deep learning shows new features unseen in non-deep learning data-driven models. It can also help to address some old challenges in hydrological modeling, such as hydrological modeling under the impact of hydraulic projects and uncertainty analysis. Deep learning can be a tool for knowledge discovery. We also describe some studies for the integration of deep learning and domain knowledge in hydrology, including incorporating deep learning in physical mechanism models and physics-guided deep learning. We also highlight the challenges in applying deep learning to hydrological forecasting and propose potential opportunities. This review can provide a useful reference for further research on deep-learning-based hydrological forecasting.
Keywords:
Project Supported:
Clc Number:
Mobile website
® 2024 All Rights Reserved
冀公网安备13010502002656号
冀公网安备13010502002656号