Innovations in Hydrology: Streamflow and Flood Prediction
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 725
Special Issue Editor
Interests: streamflow prediction; cascade reservoir scheduling; water–energy–food–carbon coupling; water quality prediction; water resource management; water–wind–solar–storage multi-energy complementarity; hydrological modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the latest advancements and innovative approaches in streamflow and flood prediction within the field of hydrology. The integration of physical hydrological models with machine learning techniques, as well as deep learning ensemble predictions, has opened new avenues for more accurate and reliable forecasting. This Special Issue aims to bring together cutting-edge research that addresses the challenges and opportunities in this domain, facilitating the exchange of ideas and knowledge among the scientific community.
Main themes include, but are not limited to, the following:
(1) Integration of Physical and Machine Learning Models: This includes studies on how machine learning can enhance the parameterization, calibration, and uncertainty quantification of physical models, as well as how physical understanding can improve the interpretability and generalization of machine learning models for streamflow and flood prediction.
(2) Deep Learning Ensemble Predictions: This involves the development of ensemble strategies that combine multiple deep learning models, the integration of deep learning with other forecasting methods, and the evaluation of ensemble performance in terms of accuracy, reliability, and computational efficiency.
(3) Case Studies and Applications: Real-world case studies demonstrating the successful implementation of these innovative approaches in different hydro-climatic regions are highly valuable.
(4) Uncertainty Analysis and Risk Assessment: This includes the development of probabilistic forecasting frameworks and the use of uncertainty analysis to support robust water management strategies.
In conclusion, this Special Issue on streamflow and flood prediction will serve as a vital platform. By highlighting the integration of physical models and machine learning, as well as deep learning ensembles, we strive to advance this field. These techniques will improve prediction accuracy, facilitating better flood prevention and water resource management, thus enhancing the resilience and sustainability of communities and ecosystems amid hydrological uncertainties.
Dr. Zhaocai Wang
Guest Editor
Manuscript Submission Information
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Keywords
- streamflow prediction
- flood prediction
- physical hydrological models
- machine learning
- deep learning
- uncertainty quantification
- flood risk assessment
- water resource management
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