CoRAL Lab at Virginia Tech

Compound flood hazard and Risk Assessment in Low-lying areas (CoRAL).

CoRAL_Logo.png

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.
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

Selected Publications

  1. From local to regional compound flood mapping with deep learning and data fusion techniques
    David F Muñoz, Paul Muñoz, Hamed Moftakhari, and 1 more author
    Science of the Total Environment, 2021
  2. Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models
    David F Muñoz, Hamed Moftakhari, and Hamid Moradkhani
    Hydrology and Earth System Sciences, 2024
  3. Fusing multisource data to estimate the effects of urbanization, sea level rise, and hurricane impacts on long-term wetland change dynamics
    David F Muñoz, Paul Muñoz, Atieh Alipour, and 3 more authors
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
  4. Predicting the evolution of extreme water levels with long short-term memory station-based approximated models and transfer learning techniques
    Samuel Daramola, David F Muñoz, Paul Muñoz, and 2 more authors
    Water Resources Research, 2025