Machine Learning Models for Hydrological Inference: A Case Study for Flood Events
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 568
Special Issue Editor
Interests: flood early warning; flood risk management; machine learning; data integration; big data; climate change; hydrological modelling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Floods represent one of the most widespread and destructive natural hazards worldwide, posing significant threats to human life, infrastructure, and socioeconomic systems. With the intensification of climate change and rapid urbanization, the frequency and severity of flood events are increasing, thereby presenting substantial challenges to existing flood monitoring, forecasting, and response systems.
In this context, machine learning and other advanced intelligent technologies are offering new opportunities for hydrological inference and flood modeling, marking a paradigm shift in traditional hydrological practices. In recent years, machine learning techniques have demonstrated considerable potential in various aspects of flood-related studies, including flood forecasting, streamflow simulation, rainfall-runoff modeling, data assimilation, and filling the gaps of hydrological records. These data-driven approaches have contributed to enhanced accuracy and efficiency in hydrological inference.
However, several challenges remain, such as limited model interpretability, variability in data quality, and the lack of generalizability across diverse geographic regions. This Special Issue thus aims to bring together researchers, engineers, and practitioners from the fields of hydrology, artificial intelligence, disaster risk reduction, and related disciplines to share their latest insights, methodological innovations, and practical experiences in applying machine learning to flood events.
Dr. Li Zhou
Guest Editor
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Keywords
- development and optimization of machine learning models for flood prediction and early warning
- multi-source data fusion and remote sensing applications in flood monitoring
- hydrological response modeling under extreme rainfall conditions
- interpretability, transferability, and uncertainty quantification of machine learning models
- small-sample learning and transfer learning in data-scarce regions
- hybrid modeling approaches that integrate physical-based and machine learning models
- case studies and system-level applications of machine learning in real-world flood management contexts
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