Abstract
No public abstract is available from the verified source records.
Hyeontae Moon, Kyung-Tak Kim, Gilho Kim
Abstract
Authors
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
unpaywall
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
A surrogate machine learning modeling approach for enhancing the efficiency of urban flood modeling at metropolitan scales
10.1016/j.scs.2025.106277 · 2025
A review of recent advances in urban flood research
2023
Transfer learning using the global Caravan dataset for developing a local river streamflow prediction model
10.1016/j.envsoft.2025.106691 · 2025
Flood risk analyses–how detailed do we need to be–
10.1007/s11069-008-9277-8 · 2009
A probabilistic modelling system for assessing flood risks
10.1007/s11069-005-8603-7 · 2006
The sensitivity of urban pluvial flooding to the temporal distribution of rainfall within design storms
10.1111/jfr3.70097 · 2025
Leveraging coupled hydrodynamic with data-driven GeoAI models for advancing systemic compound flood risk evaluation in coastal urban areas
2025
A machine learning-based prediction-to-map framework for rapid and accurate spatial flood prediction
10.1038/s44304-025-00122-2 · 2025
Unveiling the limits of deep learning models in hydrological extrapolation tasks
10.5194/hess-29-5871-2025 · 2025
Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates
10.1029/2020wr028673 · 2021
Deep learning methods for flood mapping: a review of existing applications and future research directions
2022
Random search for hyper-parameter optimization
2012
Data driven real-time prediction of urban floods with spatial and temporal distribution
2024
Development and comparison of two fast surrogate models for urban pluvial flood simulations
10.1007/s11269-018-1959-8 · 2018
Impact of the rainfall duration and temporal rainfall distribution defined using the Huff curves on the hydraulic flood modelling results
10.3390/geosciences8020069 · 2018
Cumulative storm rainfall distributions: comparison of Huff curves
2003
Inside or outside: quantifying extrapolation across river networks
10.1029/2018wr023378 · 2018
Mapping gully erosion variability and susceptibility using remote sensing, multivariate statistical analysis, and machine learning in South Mato Grosso
10.3390/geosciences12060235 · 2022
A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment
10.1016/j.advwatres.2020.103821 · 2021
Recent advances and future challenges in urban pluvial flood modelling
10.1080/1573062x.2024.2446528 · 2025
Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis
10.1016/j.jhydrol.2022.128086 · 2022
XGBoost: a scalable tree boosting system
2016
Fine-tuning long short-term memory models for seamless transition in hydrological modelling: from pre-training to post-application
10.1016/j.envsoft.2025.106350 · 2025
Multi-dimensional flood damage analysis (I): principle and procedure
2006
Real-time short-range flood forecasting based on a watershed scale 2-D hydrodynamic model and high-resolution precipitation forecast ensemble
10.1016/j.jhydrol.2024.132564 · 2025
Forecasting flood hazards in real time: a surrogate model for hydrometeorological events in an Andean watershed
10.5194/nhess-20-3261-2020 · 2020
Measuring compound flood potential from river discharge and storm surge extremes at the global scale
10.5194/nhess-20-489-2020 · 2020
A method for assessing urban flood impacts by combining hydrodynamic models with entity semantic evolution
10.1080/17538947.2025.2515268 · 2025
Application of machine learning-based surrogate models for urban flood depth modeling in Ho Chi Minh City, Vietnam
10.1016/j.asoc.2023.111031 · 2024
Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates
10.1007/s11069-010-9675-6 · 2011
A framework for the evaluation of flood inundation predictions over extensive benchmark databases
10.1016/j.envsoft.2025.106786 · 2026
Developing flood mapping procedure through optimized machine learning techniques. Case study: prahova river basin, Romania
2024
Physics-informed neural networks as surrogate models of hydrodynamic simulators
10.1016/j.scitotenv.2023.168814 · 2024
A new dataset of river flood hazard maps for Europe and the Mediterranean Basin
10.5194/essd-14-1549-2022 · 2022
Fast urban flood modeling informing response decisions: model development and future perspectives
10.1016/j.aei.2025.104152 · 2026
Convolutional neural networks with transfer learning for natural river flow prediction in ungauged basins
10.1038/s41598-025-07088-1 · 2025
Tackling random fields non-linearities with unsupervised clustering of polynomial chaos expansion in latent space: application to global sensitivity analysis of river flooding
10.1007/s00477-021-02060-7 · 2022
Integrating net rainfall calculation in deep learning-based surrogate modeling frameworks for 2D flood prediction
10.1016/j.jhydrol.2025.133632 · 2025
Urbanization impacts on flood risks based on urban growth data and coupled flood models
10.1007/s11069-020-04480-0 · 2021
Anthropogenic intensification of short-duration rainfall extremes
10.1038/s43017-020-00128-6 · 2021
A rapid flood inundation modelling framework using deep learning with spatial reduction and reconstruction. Environ
10.1016/j.envsoft.2021.105112 · doi-reference
A study on compound flood prediction and inundation simulation under future scenarios in a coastal city
10.1016/j.jhydrol.2023.130475 · doi-reference
Impact of urbanization on regional rainfall–runoff processes: case study in Jinan City, China
10.3390/rs15092383 · doi-reference
A hybrid method based on proper orthogonal decomposition and deep neural networks for flow and heat field reconstruction
10.1016/j.eswa.2024.123137 · doi-reference
Deep learning of flood forecasting by considering interpretability and physical constraints
10.5194/hess-29-5955-2025 · doi-reference
High temporal resolution urban flood prediction using attention-based LSTM models
10.1016/j.jhydrol.2023.129499 · doi-reference
Hybrid surrogate model for timely prediction of flash flood inundation maps caused by rapid river overflow
10.3390/forecast4010007 · doi-reference
Training machine learning surrogate models from a high-fidelity physics-based model: application for real-time street-scale flood prediction in an urban coastal community
10.1029/2019wr027038 · doi-reference
Urban flood modeling and forecasting with deep neural operator and transfer learning
10.1016/j.jhydrol.2025.133705 · doi-reference
A hybrid surrogate model for real-time coastal urban flood prediction: an application to Macao
10.1016/j.jhydrol.2024.131863 · doi-reference
A ConvLSTM-based model for urban flood prediction under dynamic rainfall patterns and exploration on its extrapolation capability
10.1007/s13753-025-00685-8 · doi-reference
Physics-guided deep learning for spatiotemporal evolution of urban pluvial flooding
10.3390/w17081239 · doi-reference
New insights into US flood vulnerability revealed from flood insurance big data
10.1038/s41467-020-15264-2 · doi-reference
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
10.3354/cr030079 · doi-reference
Hydrodynamic modelling and flood risk analysis of urban catchments under multiple scenarios: a case study of Dongfeng Canal district, Zhengzhou
10.3390/ijerph192214630 · doi-reference
Developing a flood risk assessment model with genetic algorithm-based weights
10.1016/j.jhydrol.2024.131902 · doi-reference
Rapid prediction of flood inundation by interpolation between flood library maps for real-time applications
10.1016/j.jhydrol.2022.127735 · doi-reference
Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem
10.1016/j.jcp.2019.01.031 · doi-reference
Uncertainty in flood damage estimates and its potential effect on investment decisions
10.5194/nhess-16-1-2016 · doi-reference
Multi-variable flood damage modelling with limited data using supervised learning approaches
10.5194/nhess-17-1683-2017 · doi-reference
Invited perspectives: how machine learning will change flood risk and impact assessment
10.5194/nhess-20-1149-2020 · doi-reference
Near real-time flood mapping with weakly supervised machine learning
10.3390/rs15133263 · doi-reference
Building a generalized pre-training model to predict river water-level from radar rainfall
10.3390/w17243449 · doi-reference
UIDS: a Matlab-based urban flood model considering rainfall-induced and surcharge-induced inundations
10.1016/j.envsoft.2024.106132 · doi-reference
Designing an inundation monitoring and real-time urban flood forecasting system: a synthetic study
10.1016/j.jhydrol.2026.135520 · doi-reference
A data-driven framework for flood mitigation: transformer-based damage prediction and reinforcement learning for reservoir operations
10.3390/w17203024 · doi-reference
Coping with floods: preparedness, response and recovery of flood-affected residents in Germany in 2002
10.1623/hysj.52.5.1016 · doi-reference
Flood inundation modelling: a review of methods, recent advances and uncertainty analysis
10.1016/j.envsoft.2017.01.006 · doi-reference
Uncertainty and sensitivity analysis of the HAZUS-MH flood model
10.1061/(asce)nh.1527-6996.0000167 · doi-reference
An efficient hybrid downscaling framework to estimate high-resolution river hydrodynamics
10.5194/hess-29-3833-2025 · doi-reference
A surrogate modeling method for distributed land surface hydrological models based on deep learning
10.1016/j.jhydrol.2023.129944 · doi-reference
Improving flood forecasts for better flood preparedness in the UK (and beyond)
10.1111/geoj.12103 · doi-reference
Enhancing cross-regional transferability of super-resolution-based flood surrogate models for data-scarce catchments
10.1016/j.watres.2026.125799 · doi-reference
A comprehensive review of deep learning applications in hydrology and water resources
10.2166/wst.2020.369 · doi-reference
Closing the domain gap: can pseudo-labels from synthetic UAV data enable real-world flood segmentation?
10.3390/s25123586 · doi-reference
A comprehensive review of machine learning and deep learning methods for flood inundation mapping
10.3390/earth7020044 · doi-reference
Study of simulation in storm sewer system of Zhenjiang urban by InfoWorks ICM model
10.4028/www.scientific.net/amm.193-194.683 · doi-reference
National-scale flood hazard data unfit for urban risk management
10.1029/2024ef004549 · doi-reference
A physically informed domain-independent data-driven inundation forecast model
10.1016/j.watres.2025.124819 · doi-reference
HAZUS-MH flood loss estimation methodology. I: overview and flood hazard characterization
10.1061/(asce)1527-6988(2006)7:2(60) · doi-reference