Application of Machine Learning and Satellite Remote Sensing in Flood Risk Assessment
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".
Deadline for manuscript submissions: 20 May 2026 | Viewed by 10
Special Issue Editors
Interests: remote sensing; machine learning algorithms; flood detection; natural and urban environment
Special Issues, Collections and Topics in MDPI journals
Interests: flood mapping; multi hazard analysis; machine learning; remote sensing image analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Floods constitute one of the most costly natural disasters worldwide, leading to loss of life, extensive damage to housing and livelihoods, and profound impacts on biodiversity. Globally, flood events have resulted in economic losses amounting to billions of dollars. Over recent decades, remote sensing has become a critical tool for assessing flood risk, providing synoptic observations of the extent and impact of flooding by using satellite platforms and, increasingly, unmanned aerial systems. The integration of machine learning with remote sensing imagery enables more accurate and sophisticated analyses of the spatial and temporal dynamics of flooding.
This Special Issue invites contributions that advance the state of the art of machine learning applications to remote sensing for flood monitoring, mapping, and risk assessment. We welcome original research articles, reviews, and case studies that highlight methodological innovations, novel applications, and technological advancements that enhance our understanding and management of flood hazards.
Potential themes for submission include, but are not limited to, the following:
- Deep learning approaches for flood detection and mapping;
- Real-time flood forecasting and early warning systems;
- Multi-sensor data fusion (e.g., optical, SAR, LiDAR, UAV imagery);
- Spatio-temporal modelling of flood dynamics;
- Transfer learning and domain adaptation for flood-prone regions;
- Integration of remote sensing and socio-economic data for risk assessment;
- Advances in cloud-based and high-performance computing for large-scale flood analysis;
- Case studies demonstrating operational applications and decision-support systems.
Dr. Alan Woodley
Dr. Chandrama Sarker
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- flood risk
- flood mapping
- remote sensing
- machine learning algorithms
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