CoRAL VT

🚨We’re proud to share Md. Shadman Sakib’s first lead-author article from his PhD. We investigated how unresolved nonlinear interactions📈📉 in total water levels🌊 evolve over annual scales & during tropical cyclones for the Chesapeake Bay. @virginiatech.bsky.social #GoHokies doi.org/10.1016/j.ad...

Grateful with Fredricka Whitfield and her team from CNN for the opportunity to talk about the CoRAL Lab (https://lnkd.in/g3q3b4EN). | David F. Muñoz Grateful with Fredricka Whitfield and her team from CNN for the opportunity to talk about the CoRAL Lab (https://lnkd.in/g3q3b4EN). My research group combines physics-based and data-driven models #AI ...

Grateful with Fredricka Whitfield and her team from @cnn.com for the opportunity to talk about the CoRAL Lab. My research group combines physics-based and data-driven models #AI to improve the prediction of compound flood hazards and risks @virginiatech.bsky.social www.linkedin.com/posts/davidm...

A satellite image shows a large hurricane swirling over the ocean, with a clearly defined eye and dense cloud bands. Below the image, text reads: “VIRGINIA TECH RESEARCHERS ARE WORKING TO IMPROVE HURRICANE PREPAREDNESS & FLOOD WARNINGS” The Virginia Tech logo is displayed at the bottom center.

#VirginiaTech researchers are teaming up across disciplines to better predict hurricane-driven flooding and help communities prepare for extreme weather.

Here's how. ➡️ news.vt.edu/articles/2025/07/hurricane-research-experts-2025-Atlantic-season

How researchers use new tools to predict future flooding | Latest Weather Clips | FOX Weather Assistant Professor at Virginia Tech, David F. Muñoz, joins FOX Weather to share how researchers use deep learning to predict future floods.

June 1st marks the start of 2025 Atlantic hurricane season. ⛈️🌀 David F. Muñoz spoke to @foxweather.bsky.social to share how researchers use deep learning to predict future flooding.

Researchers use deep learning to predict flooding this hurricane season A team led by Virginia Tech graduate student Samuel Daramola developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, ...

A great collaboration with colleagues from @vtengineering.bsky.social & @hydr-vub.bsky.social We push the boundaries of transfer learning for #extreme water level prediction 🌊 across scales! Stayed tuned for a follow up study at the global scale 🤓 🌎🌍🌏
news.vt.edu/articles/202...

🚨🚨Free access to Samuel Daramola’s latest research is available now! Check how customized deep learning architectures 🤖 can learn (and predict) compound flood dynamics 🌊 in space🌌 & time⏳for multiple cyclonic events🌀⛈️ authors.elsevier.com/c/1l0tC4sKhE...
@virginiatech.bsky.social

Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques We present a deep learning framework that accurately predicts the evolution of cyclone-induced water levels across multiple domains An attention mechanism enhances the framework's recognition of ...

We are super excited to share Samuel’s first PhD paper 🤓📜Here, we address the limitations of transfer learning and show that, despite site-specific conditions, DL models can generalize spatiotemporal patterns of coastal water levels 🌊 from distinctive tropical cyclones 🌀
dx.doi.org/10.1029/2024...