Advancing Flood Detection, Monitoring & Simulation: Integrating Machine Learning, Remote Sensing & Hydrodynamic Model
A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 110
Special Issue Editors
Interests: flood simulation and detection; machine learning; hydrodynamic model; hydrology and water resources
2. TOBIN, Block 10-4, Blanchardstown Corporate Park, D15 X98N Dublin, Ireland
Interests: hydrology; environmental; floods; remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increasing severity of global flood events, exacerbated by climate change and urbanization, necessitates the development of advanced methodologies for flood detection, monitoring, and simulation. Hydrodynamic models, remote sensing, and machine learning represent effective approaches to flood risk management. Hydrodynamic models simulate flow dynamics and inundation, supporting infrastructure planning. Remote sensing enables real-time, large-scale mapping of flood extents, overcoming the limitations of ground-based observations. Machine learning algorithms extract complex patterns from geospatial and hydrological data, thereby enhancing flood susceptibility modeling, improving early warning accuracy, and facilitating rapid post-event analysis. This synergistic approach facilitates high-resolution, real-time forecasting and dynamic risk assessment, addressing critical gaps in traditional methods. It is vital for mitigating catastrophic socioeconomic losses and informing resilient urban and environmental policies, offering proactive, data-driven strategies to reduce vulnerability and enhance adaptive capacity. Ultimately, this integration safeguards lives, livelihoods, and sustainable development against intensifying hydrological extremes. The results guide for managing urban rainstorm inundation and improving the timeliness and efficiency of urban flood emergency decision-making.
The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about innovative methodologies in flood detection, monitoring, and simulation, applying hydrodynamic models, remote sensing, and machine learning.
The issue aims to address evolving challenges in flood resilience and adaptive strategies in the context of climate change, ultimately developing solutions to protect communities, infrastructure, and sustainable development from extreme rainstorm floods.
This Special Issue will welcome manuscripts that link the following themes:
- Machine learning-enhanced flood early warning systems and flood forecasting;
- Hydrodynamic and hydrological modeling for urban and catchment scale flood inundation and risk assessment;
- Remote sensing and UAV-based real-time flood inundation mapping and monitoring;
- Dynamic flood vulnerability assessment using geospatial analytics;
- Integrated machine learning and remote sensing approaches for flood intelligent detection;
- Urban flood resilience evaluation using integrated computational models and AI.
We look forward to receiving your original research articles and reviews.
Dr. Hao Han
Dr. Aristoteles Tegos
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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 detection, monitoring and simulation
- machine learning
- remote sensing
- hydrodynamic model
- extreme rainfall events
- urban and catchment scale flooding
- inundation simulation
- flood risk assessment
- urban resilience
- real-time forecasting
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