All Stories

  1. Portable multi-GPU urban flood modelling via MPI-OpenACC: evaluating acceleration, scaling, and workload-per-GPU

    Article • Environmental Modelling & Software, May 2026, Elsevier

    Seongjin Noh

  2. Accelerating Urban Flood Data Assimilation: Coupling Physics Guided AI Emulators with Real Time Observations

    Article • March 2026, Copernicus GmbH

    Seongjin Noh

  3. Hybrid Physics-AI Approaches for Urban Flood Prediction: GPU Hydrodynamics, Data Assimilation, and AI Surrogates

    Article • March 2026, Copernicus GmbH

    Seongjin Noh

  4. Integrating WRF-Hydro and DART for Ensemble Streamflow Forecasting in a Highly Regulated Basin with Anthropogenic Intervention

    Article • March 2026, Copernicus GmbH

    Seongjin Noh

  5. Multi-GPU acceleration of high-resolution and large-scale urban flood modelling using MPI–OpenACC

    Article • March 2026, Copernicus GmbH

    Seongjin Noh

  6. Scale-Aware Machine Learning for Precipitation Downscaling: Impact on Regional Applications in Europe

    Article • March 2026, Copernicus GmbH

    Seongjin Noh

  7. Adaptive Time-Lagged Ensemble for Short-Range Streamflow Prediction Using WRF-Hydro and LDAPS

    Article • Water, January 2026, MDPI AG

    Seongjin Noh

  8. High-resolution urban flood risk mapping using indicator cokriging integrated with physical modelling and observational data

    Article • Journal of Hydroinformatics, December 2025, IWA Publishing

    Seongjin Noh

  9. Comparative Evaluation of Deep Learning–Based Super–Resolution Models for Urban Flood Mapping

    Article • Journal of Flood Risk Management, October 2025, Wiley

    Seongjin Noh

  10. Evaluating the Effects of Spatial Resolution on 2D Pluvial Flood Modeling in Urban Built Environments

    Article • Journal of Flood Risk Management, July 2025, Wiley

    Seongjin Noh

  11. Physics-Guided Deep Learning for Spatiotemporal Evolution of Urban Pluvial Flooding

    Article • Water, April 2025, MDPI AG

    Seongjin Noh

  12. Assessing the impacts of climate change on extreme hydrometeorological events using an integrated WRF and WRF-Hydro framework

    Article • March 2025, Copernicus GmbH

    Seongjin Noh

  13. Deep Learning-Based Urban Pluvial Flood Modeling using High-resolution Physical Information

    Article • March 2025, Copernicus GmbH

    Seongjin Noh

  14. Developing a Deep Learning-Based Super-Resolution Urban Flood Model: Towards Scalable and Reliable Hydrological Predictions

    Article • March 2025, Copernicus GmbH

    Seongjin Noh

  15. Exploring the Role of Multivariate Observations in Urban Flood Prediction: A Data Assimilation Approach

    Article • March 2025, Copernicus GmbH

    Seongjin Noh

  16. Integrating Citizen Data in Urban Flood Risk Modeling: Insights from synthetic experiments

    Article • March 2025, Copernicus GmbH

    Seongjin Noh

  17. Editorial to the Special Issue “Recent Advances in Hydrological Modeling”

    Article • Hydrology, July 2024, MDPI AG

    Seongjin Noh

  18. Evaluating the effects of spatial resolution on urban pluvial flood modelling

    Article • July 2024, Wiley

    Seongjin Noh

  19. Assessment of the Impact of Spatial Variability on Streamflow Predictions Using High-Resolution Modeling and Parameter Estimation: Case Study of Geumho River Catchment, South Korea

    Article • Water, February 2024, MDPI AG

    Seongjin Noh

  20. Enhancing Tidal Wave Predictions for the Estuary of the Nakdong River Using a Fixed-Lag Smoother

    Article • Energies, December 2022, MDPI AG

    Seongjin Noh

  21. Cellular Automata-based high-resolution hydrological modeling for urban digital water information

    Article • March 2022, Copernicus GmbH

    Seongjin Noh

  22. Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

    Article • Hydrology and Earth System Sciences, September 2021, Copernicus GmbH

    Seongjin Noh

  23. Ensemble Streamflow Data Assimilation using WRF-Hydro and DART: Hurricane Florence Flooding

    Article • March 2021, Copernicus GmbH

    Seongjin Noh

  24. An ensemble of deep learning models with data assimilation for hydrologic forecasting

    Article • March 2021, Copernicus GmbH

    Seongjin Noh

  25. Identification of Inundation Using Low-Resolution Images from Traffic-Monitoring Cameras: Bayes Shrink and Bayesian Segmentation

    Article • Water, June 2020, MDPI AG

    Seongjin Noh

  26. Ensemble Streamflow Assimilation with Coupled WRF-Hydro and DART

    Article • March 2020, Copernicus GmbH

    Seongjin Noh

  27. Improving flood forecasting using conditional bias-penalized ensemble Kalman filter

    Article • Journal of Hydrology, August 2019, Elsevier

    Seongjin Noh

  28. Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

    Article • Water, March 2019, MDPI AG

    Seongjin Noh

  29. High-resolution hydrologic forecasting for very large urban areas

    Article • Journal of Hydroinformatics, February 2019, IWA Publishing

    Hamideh Habibi, Seongjin Noh

  30. Hybrid Numerical Scheme of Preissmann Slot Model for Transient Mixed Flows

    Article • Water, July 2018, MDPI AG

    Seongjin Noh

  31. Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization

    Article • Environmental Modelling & Software, May 2018, Elsevier

    Seongjin Noh

  32. Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

    Article • Hydrological Sciences Journal, February 2018, Taylor & Francis

    Seongjin Noh, Dr Leonardo Alfonso

  33. Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions

    Article • Hydrology, January 2018, MDPI AG

    Seongjin Noh

  34. Ensemble urban flood simulation in comparison with laboratory-scale experiments: Impact of interaction models for manhole, sewer pipe, and surface flow

    Article • Advances in Water Resources, November 2016, Elsevier

    Seongjin Noh

  35. Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model

    Article • Journal of Hydrologic Engineering, December 2013, American Society of Civil Engineers (ASCE)

    Seongjin Noh

  36. DEVELOPMENT OF A HYDROLOGICAL MODELING FRAMEWORK FOR DATA ASSIMILATION WITH PARTICLE FILTERS

    Article • Journal of JSCE, January 2013, Japan Society of Civil Engineers

    Seongjin Noh

  37. Simultaneous estimation of inflow and channel roughness using 2D hydraulic model and particle filters

    Article • Journal of Flood Risk Management, December 2012, Wiley

    Seongjin Noh

  38. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

    Article • Hydrology and Earth System Sciences, October 2012, Copernicus GmbH

    Seongjin Noh

  39. Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization

    Article • Hydrology and Earth System Sciences, October 2011, Copernicus GmbH

    Seongjin Noh

  40. Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization

    Article • Hydrology and Earth System Sciences Discussions, April 2011, Copernicus GmbH

    Seongjin Noh

  41. DUAL STATE-PARAMETER UPDATING SCHEME ON A CONCEPTUAL HYDROLOGIC MODEL USING SEQUENTIAL MONTE CARLO FILTERS

    Article • Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering), January 2011, Japan Society of Civil Engineers

    Seongjin Noh

  42. Application of WEP Model to the Cheonggyecheon Watershed

    Article • Journal of Korea Water Resources Association, August 2005, Korea Water Resources Association

    Seongjin Noh