Flood Forecasting

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Flood forecasting estimates how water levels, discharge, and inundation will change across river basins, coasts, and cities, giving communities and infrastructure managers time to act. Approaches range from hydrodynamic models and rainfall–runoff models to LSTMs, transformers, graph neural networks, and fast surrogates trained on simulated or observed floods; some combine these with terrain, river networks, satellite data, or physical constraints. Evaluation spans gauge-based benchmarks such as USGS basins and GloFAS, as well as event detection and street- or basin-scale inundation maps, with probabilistic methods increasingly used to represent uncertainty. A central challenge is maintaining reliable forecasts for extreme or unfamiliar events and data-sparse locations, while validating fast AI systems with real-time observations and uncertain rainfall forecasts before operational use.

Latest papers

18 papers1 paper in the last four weeks, down 75% on the four weeks before. 0.0% of all new papers.

C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories
Sep 30, 2026Yanjie Tong, Phillip Si, Yuan Qiu +1Surrogate Modeling Flood Inundation Mapping
School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street Northwest, Atlanta, 30308, GA, USA
Distributed Hydrological Modeling in the Feature Space
Sep 26, 2026Mohamad Hakam Shams Eddin, Maria Luisa Taccari, Yikui Zhang +3Streamflow Forecasting Hydrology
Max Planck Institute for Biogeochemistry, Germany · European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom · Institute of Bio- and Geosciences Agrosphere (IBG-3), Research Centre Jülich, Germany +2
Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale
Sep 19, 2026Hong Zhang, John K. Hutchison, Rao Kotamarthi +7Streamflow Forecasting Time Series Forecasting
Argonne National Laboratory, Lemont, IL 60439, USA
Knowledge-Graph-Augmented Chronos-2 for HEC-RAS Surrogate Forecasting
Sep 18, 2026Edward Holmberg, Elias Ioup, Mahdi AbdelguerfiSurrogate Modeling Flood Forecasting
Canizaro-Livingston Gulf States Center for Environmental Informatics New Orleans, LA, USA · Center for Geospatial Sciences Naval Research Laboratory Stennis Space Center, Mississippi, USA
Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analytics
Sep 5, 2026Jie Zhang, Qiang Ni, David Windridge +1Flood Forecasting Digital Twins
Lancaster University, Lancaster, United Kingdom · Middlesex University, London, United Kingdom
Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems
Aug 3, 2026Liangjun You, Min Wu, Orlando Woods +1Multivariate Time Series Forecasting Dynamic Graph Learning
City University of Hong Kong (Dongguan) China · A*STAR Singapore · Singapore Management University Singapore
Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibility
Jul 27, 2026Luc DCosta, Yidi Wang, Jonathan L. Goodall +1Convolutional RNNs Flood Inundation Mapping
Chandra Robot Autonomy Lab, Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA · Link Lab, Multidisciplinary Research Center, University of Virginia, Charlottesville, VA 22904, USA · Hydroinformatics Research Group, Department of Civil and Environmental Engineering, University of Virginia, Charlottesville, VA 22904, USA
HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows
Jul 27, 2026Qingyi Yang, Siqian Qiu, Bing Li +20Agentic Workflows Spatiotemporal Forecasting
Department of Civil and Environmental Engineering (D.I.C.A.), Politecnico di Milano, 20133 Milano MI, Italy · College of Hydraulic and Environmental Engineering, China Three Gorges University, 443002 Yichang, China · 3Independent Researcher, Shandong, 252300, China +16
Context-Aware Concept Distillation for Trustworthy Flood Prediction
Jul 25, 2026Eli Levinkopf, Efrat Morin, Claudia V. GoldmanNeural Surrogate Modeling Explainable Artificial Intelligence
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel · Institute of Earth Sciences, The Hebrew University of Jerusalem, Israel · Hebrew University Business School, The Hebrew University of Jerusalem, Israel
Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation models
Jun 3, 2026Lipai Huang, Adithi Srinath, Manas Singh +2Surrogate Modeling OOD Generalization
Urban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA · Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA · Resilitix Intelligence LLC, Houston, TX, USA +1
Physics-Informed Machine Learning for Short-Term Flood Prediction
Jun 2, 2026Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-BeniLong Short-Term Memory Networks Physics-Informed ML
North Carolina A&T State University · Greensboro, NC 27407, USA
HaorFloodAlert: A 72-Hour Machine Learning Early Warning System for Flash Floods in Bangladesh's Haor Wetlands
May 19, 2026Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely, Md. Samiul Alim +1Spatiotemporal Forecasting Flood Forecasting
Department of Computer Science and Engineering, RTM Al-Kabir Technical University, Sylhet-3100, Bangladesh · Department of Computer Science and Engineering, North East University Bangladesh, Sylhet, Bangladesh · Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh
Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems
May 19, 2026Jimeng ShiRetrieval-Augmented Generation Explainable Artificial Intelligence
FLORIDA INTERNATIONAL UNIVERSITY Miami, Florida
Toward AI-Driven Digital Twins for Metropolitan Floods: A Conditional Latent Dynamics Network Surrogate of the Shallow Water Equations
May 13, 2026Phillip Si, Yuan Qiu, Omar Sallam +4Digital Twins PDE Surrogate Modeling
School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street Northwest, Atlanta, 30308, GA, USA · Environmental Science Division, Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
Apr 10, 2026Prasad Nimantha Madusanka Ukwatta Hewage, Midhun Chakkravarthy, Ruvan Kumara AbeysekaraPhysics-Informed ML Hybrid Quantum-Classical ML
Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Selangor, Malaysia · BCAS Campus, Colombo, Sri Lanka
Toward an Operational GNN-Based Multimesh Surrogate for Fast Flood Forecasting
Apr 3, 2026Valentin Mercier, Serge Gratton, Lapeyre Corentin +1Flood Forecasting Neural Surrogate Modeling
Toulouse INP, IRIT, EPE UT · Université de Toulouse, INP, IRIT, Toulouse, France · IRIT, EPE UT, Toulouse INP +3
HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks
Feb 27, 2026Noujoud Nadera, Hadi Majed, Stefanos Giaremis +4Spatiotemporal Forecasting Flood Forecasting
Center of Computation and Technology, Louisiana State University, Baton Rouge, 70803 LA, US · Saint-Joseph University of Beirut, Beirut, 1104, Lebanon · Department of Physics, Aristotle University of Thessaloniki, Thessaloniki , 54124, Greece +3
Uncovering Insights of Compound Flooding with Data-Driven AI
Jun 4, 2025Xu Zheng, Chaohao Lin, Sipeng Chen +7Spatiotemporal Forecasting Flood Forecasting
Florida International University Miami, FL, USA · Florida State University Tallahassee, FL, USA · UIUC Champaign, IL, USA +3

Sep 30, 2026J/K move · Enter open · S save

C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories

Yanjie Tong, Phillip Si, Yuan Qiu, Peng Chen

School of Computational Science and Engineering, Georgia Institute of Technology, 756 West Peachtree Street Northwest, Atlanta, 30308, GA, USA

Surrogate Modeling Flood Inundation Mapping Digital Twins Spatiotemporal Forecasting Flood Forecasting Hydrology

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Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time measurements come from only a handful of stream gauges, and high-resolution hydrodynamic models are too costly to rerun each time new data arrive or to run as large ensembles. We present C-STRIDE, an observation-driven AI digital twin that turns short records from a few stream gauges, together with terrain and rainfall, into basin-wide maps of water depth and extends these predictions up to a day ahead. It is trained on simulations from a calibrated two-dimensional hydrodynamic model and needs no separate data-assimilation step. In the Des Plaines River basin near Chicago, six gauges inform predictions over 4.2 million 30-m grid cells. Terrain improves the predictions most, rainfall keeps errors from growing over longer horizons, and together they reduce errors by about 40% compared with gauge records alone. When future rainfall is known, errors remain near 15% one day ahead, compared with nearly 40% without rainfall. Given real instead of simulated gauge records, the model shifts its predictions toward the observed hydrographs at three of six gauges without retraining, and it runs about 150 times faster than the hydrodynamic model. These results show how sparse gauges, terrain, and rainfall can be combined into fast, continuously updated flood predictions, a step toward operational flood digital twins that still requires testing with real-time data and rainfall forecasts.