CoRAL Lab at Virginia Tech
Compound flood hazard and Risk Assessment in Low-lying areas (CoRAL).

The CoRAL Lab integrates physics-informed and data-driven models to understand complex compound extreme dynamics in human and natural systems. We are interested in statistical analysis, hydrodynamic modeling, machine learning, remote sensing, and data collection using drones.
Vision:
The CoRAL Lab aims at enhancing the characterization, modeling, and prediction of compound extreme dynamics at different scales as well as their impacts on vulnerable communities to flooding.
We aim at providing actionable flood hazard maps, adaptation and planning strategies that integrate green infrastructure and risk-flood information through scenario-based simulations.

News
Jul 02, 2025 | Our research on predicting extreme water levels in the U.S. Atlantic Coast has been featured by CNN. |
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Jun 02, 2025 | Our research on predicting extreme water levels in the U.S. Atlantic Coast has been featured by FOX weather. |
May 02, 2025 | Md. Shadman Sakib has been selected to participate in the National Water Center Innovators Program - Summer Institute sponsored by CUAHSI and CIROH. |
Apr 02, 2025 | Dr. Muñoz participated in the EGU 2025. We collaborate with researchers from Belgium and Ecuador to advance operational hydrology in data-scarce mountainous areas using machine learning. See the abstracts 1, and 2. |
Latest Posts
Jul 24, 2025 | 10th Water Prediction Innovators Summer Institute |
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May 02, 2024 | Chris Mansky defended his master thesis |
Jul 26, 2023 | 8th Water Prediction Innovators Summer Institute |
Selected Publications
- From local to regional compound flood mapping with deep learning and data fusion techniquesScience of the Total Environment, 2021
- Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning modelsHydrology and Earth System Sciences, 2024
- Fusing multisource data to estimate the effects of urbanization, sea level rise, and hurricane impacts on long-term wetland change dynamicsIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
- Predicting the evolution of extreme water levels with long short-term memory station-based approximated models and transfer learning techniquesWater Resources Research, 2025