Machine Learning Applications for Hydrological Prediction, Watershed Processes and Sustainable Water Management
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 193
Editors
Interests: hydrological modeling; climate change impacts; flood risk management; artificial intelligence in water systems; river basin and ecosystem sustainability
Interests: hydrological modeling; flood risk management; climate change adaptation; machine learning applications; water resources sustainability
Special Issue Information
Dear Colleagues,
Hydrological modeling has become an essential tool for understanding watershed processes, predicting hydrological responses and supporting sustainable water resources management under changing environmental conditions. Recent advances in computational methods, remote sensing technologies, geospatial analysis, machine learning and process-based modeling have significantly enhanced the ability to simulate complex watershed dynamics across multiple spatial and temporal scales. However, increasing pressures from climate change, land-use transformation, urbanization and extreme hydrological events continue to challenge conventional modeling approaches. This Special Issue aims to showcase the recent developments, innovative methodologies and practical applications of advanced hydrological modeling in watershed research. Contributions that improve the understanding of rainfall-runoff processes, groundwater–surface water interactions, flood and drought assessment, sediment transport, eco-hydrological dynamics and watershed resilience under future environmental scenarios are invited. The Special Issue welcomes studies employing physically based, conceptual, data-driven, hybrid and AI-assisted modeling approaches, as well as uncertainty analysis, model calibration and decision-support frameworks. Through interdisciplinary research and novel modeling techniques, this Special Issue seeks to advance scientific knowledge and provide actionable insights for sustainable watershed management, climate adaptation and water security worldwide.
Dr. Shan-e-hyder Soomro
Dr. Caihong Hu
Dr. Chengshuai Liu
Guest Editors
Manuscript Submission Information
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Keywords
- hydrological modeling
- watershed management
- rainfall–runoff simulation
- water resource management
- climate change impacts
- flood risk assessment
- machine learning in hydrology
- eco-hydrological processes
- watershed resilience
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