Machine Learning Models for Flood Hazard Assessment
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 May 2025 | Viewed by 50
Special Issue Editor
Interests: hydrology; water resources; water management; water planning; floods; droughts; climate change; ecohydrology; statistical hydrology; hydroinformatic
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) models have emerged as valuable tools for flood hazard assessment, offering improved predictive capabilities and data-driven insights. Current state-of-the-art techniques include supervised learning algorithms, such as random forests and support vector machines, as well as advanced deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These models leverage large datasets, including meteorological, hydrological, and geographic information, to enhance flood risk predictions and enable real-time monitoring. Despite their potential, several challenges remain. Data quality and availability are significant hurdles, as comprehensive datasets may be sparse or inconsistent across different regions. Moreover, ML models often require extensive training datasets to perform accurately, which can pose difficulties in areas with limited historical flood data. Additionally, the interpretability of complex models, particularly deep learning techniques, can impede their acceptance among stakeholders who need to understand decision-making processes. Also, most scientific papers are applied to specific case studies and their methodologies are not able to be generalized. This Special Issue calls for contributions that aim to generate knowledge and applied research that contributes to the inclusion of operative ML techniques for Flood Hazard Assessment design. In addition, we are seeking manuscripts that research (but not limited to): the integration of remote sensing data and the adoption of hybrid models that combine ML with traditional hydrological models, enhancing model robustness, addressing data limitations, and fostering collaboration among interdisciplinary teams to improve flood hazard assessment and mitigation strategies.
Prof. Dr. Álvaro Sordo-Ward
Guest Editor
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Keywords
- machine learning
- artificial intelligent
- flood hazard
- flood forecasting
- flood warning
- flood risk
- models
- real time
- monitoring systems
- climate change
- new sensors
- mitigation strategies
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