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Hydrology
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Advancing Flood Detection, Monitoring & Simulation: Integrating Machine Learning, Remote...
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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 2069

Special Issue Editors

Dr. Hao Han

E-Mail Website
Guest Editor
Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
Interests: flood simulation and detection; machine learning; hydrodynamic model; hydrology and water resources
Dr. Aristoteles Tegos

E-Mail Website
Guest Editor
1. Laboratory of Hydrology and Water Resources Development, School of Civil Engineering, National Technical University of Athens, GR-15780 Athens, Greece
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 250 words) can be sent to the Editorial Office for assessment.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

21 pages, 13671 KB  
Article
Refined Simulation of Old Urban Inundation and Assessment of Stormwater Storage Capacity Based on Surface–Pipe Network–Box Culvert–River Coupled Modeling
by Ning Li, Liping Ma, Jingming Hou, Jun Wang, Xuan Li, Donglai Li, Xinxin Pan, Ruijun Cui, Yue Ren and Yangshuo Cheng
Hydrology 2025, 12(11), 280; https://doi.org/10.3390/hydrology12110280 - 28 Oct 2025
Viewed by 800
Abstract
Old urban districts, characterized by complex drainage networks, heterogeneous surfaces, and high imperviousness, are particularly susceptible to flooding during extreme rainfall. In this study, the moat drainage district of Xi’an was selected as the research area. A refined hydrologic–hydrodynamic simulation and an assessment [...] Read more.
Old urban districts, characterized by complex drainage networks, heterogeneous surfaces, and high imperviousness, are particularly susceptible to flooding during extreme rainfall. In this study, the moat drainage district of Xi’an was selected as the research area. A refined hydrologic–hydrodynamic simulation and an assessment of drainage and flood-retention capacities were conducted based on the coupled GAST–SWMM model. Results show that the model can accurately capture the rainfall–surface–pipe–river interactions and reproduce system responses under different rainfall intensities. The box culvert’s effective regulation capacity is limited to 1- to 2-year return periods, beyond which overflow rises sharply, with overflow nodes exceeding 80% during a 2-year event. The moat’s available storage capacity is 17.20 × 104 m3, sufficient for rainfall events with 5- to 10-year return periods. In a 10-year return period event, the box culvert overflow volume (12.56 × 104 m3) approaches the upper limit, resulting in overtopping. These findings provide a scientific basis for evaluating drainage efficiency and guiding flood control management in old urban districts. Full article
(This article belongs to the Special Issue Advancing Flood Detection, Monitoring & Simulation: Integrating Machine Learning, Remote Sensing & Hydrodynamic Model)
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31 pages, 11924 KB  
Article
Enhanced 3D Turbulence Models Sensitivity Assessment Under Real Extreme Conditions: Case Study, Santa Catarina River, Mexico
by Mauricio De la Cruz-Ávila and Rosanna Bonasia
Hydrology 2025, 12(10), 260; https://doi.org/10.3390/hydrology12100260 - 2 Oct 2025
Viewed by 715
Abstract
This study compares enhanced turbulence models in a natural river channel 3D simulation under extreme hydrometeorological conditions. Using ANSYS Fluent 2024 R1 and the Volume of Fluid scheme, five RANS closures were evaluated: realizable k–ε, Renormalization-Group k–ε, Shear Stress Transport k–ω, Generalized k–ω, [...] Read more.
This study compares enhanced turbulence models in a natural river channel 3D simulation under extreme hydrometeorological conditions. Using ANSYS Fluent 2024 R1 and the Volume of Fluid scheme, five RANS closures were evaluated: realizable k–ε, Renormalization-Group k–ε, Shear Stress Transport k–ω, Generalized k–ω, and Baseline-Explicit Algebraic Reynolds Stress model. A segment of the Santa Catarina River in Monterrey, Mexico, defined the computational domain, which produced high-energy, non-repeatable real-world flow conditions where hydrometric data were not yet available. Empirical validation was conducted using surface velocity estimations obtained through high-resolution video analysis. Systematic bias was minimized through mesh-independent validation (<1% error) and a benchmarked reference closure, ensuring a fair basis for inter-model comparison. All models were realized on a validated polyhedral mesh with consistent boundary conditions, evaluating performance in terms of mean velocity, turbulent viscosity, strain rate, and vorticity. Mean velocity predictions matched the empirical value of 4.43 [m/s]. The Baseline model offered the highest overall fidelity in turbulent viscosity structure (up to 43 [kg/m·s]) and anisotropy representation. Simulation runtimes ranged from 10 to 16 h, reflecting a computational cost that increases with model complexity but justified by improved flow anisotropy representation. Results show that all models yielded similar mean flow predictions within a narrow error margin. However, they differed notably in resolving low-velocity zones, turbulence intensity, and anisotropy within a purely hydrodynamic framework that does not include sediment transport. Full article
(This article belongs to the Special Issue Advancing Flood Detection, Monitoring & Simulation: Integrating Machine Learning, Remote Sensing & Hydrodynamic Model)
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