PhD Student University at Buffalo
Rapid Spatio-Temporal Prediction of Hurricane-Induced Flooding Via Knowledge-Enhanced Graph Neural Network
Co-Author: Teng Wu (University at Buffalo)
Abstract: Severe hurricane-induced flooding has caused significant loss of life and property in many coastal cities, highlighting the need for reliable and rapid flood prediction tools to assess resilience enhancement strategies. While purely data-driven models are computationally less demanding than hydrodynamic models, their training requires large datasets, and their predictions can be physically implausible. Integrating domain knowledge in the framework of machine learning can potentially address both these challenges. In this study, equation-based domain knowledge is leveraged during model training by integrating the mass conservation law into the loss function. The obtained loss function is discretized to enforce the conservation of predicted water volume at each cell within the computational domain. Since mass conservation is enforced directly on each cell of the same mesh used by the hydrodynamic model for data labeling, the water mass leaving each cell is balanced by the fluxes entering neighboring cells. In addition, the derivative terms of the loss function in a discretized format are computed from the predicted output field rather than by employing automatic differentiation. Consequently, the training efficiency is significantly improved, and the extreme sensitivity of predictions to gradient values is alleviated. Topological domain knowledge is also utilized by transforming the unstructured mesh into a graph. The obtained graph neural network (GNN) maps storm surge time series at the shoreline (with detailed topological features) to spatio-temporal inundation depth and water velocity. The proposed knowledge-enhanced GNN is able to rapidly provide physically consistent and locally accurate spatio-temporal flood predictions during a hurricane event.
Research Fellow University of Massachusetts Amherst
Global Peak Storm Surge Prediction
Co-Authors: Prateek Arora (University of California Berkley) - Presenting, Jinpai Zhao (University of Texas at Austin) and Eirik Valseth (Norwegian University of Life Sciences)
Abstract: We present a novel synthetic ADCIRC tropical cyclone simulation database with over 15,000 storms and global coverage. This database is intended to support surrogate modeling efforts and is unique in its size and global scope. We also present a surrogate model of storm surge baked on our new database that has global applicability. Our surrogate model leverages neural network architectures from computer vision and focuses on predicting maximum surge profiles. We demonstrate accurate prediction results across six different ocean basins: the North Atlantic, North Indian, South Indian, West Pacific, East Pacific, and South Pacific basins. Interestingly, we discover that surrogate models trained on the entire global dataset outperform those trained on basin-specific data, indicating that our model is able to learn location-independent storm surge dynamics. Our global model represents a step forward in storm surge emulation capability from traditional surrogate models which are tightly coupled to a small geographic region.
Associate Professor University of South Florida
Storm Surge and Coastal Inundation Nowcasts/Forecasts During Hurricanes Helene and Milton
Co-Authors: Haibo Xu (University of South Florida), Kaili Qiao (University of South Florida), Sebin John (University of South Florida), Robert H. Weisberg (University of South Florida), Jing Chen (University of South Florida), Lianyuan Zheng (University of South Florida), Sherryl Gilbert( University of South Florida), Steven A. Murawski (University of South Florida), Gary T. Mitchum (University of South Florida), and Thomas K. Frazer (University of South Florida)
Abstract: A daily automated coastal water level (storm surge) nowcast/forecast guidance system has been developed by the USF Ocean Circulation Lab based on the West Florida Coastal Ocean Model (WFCOM) and the very high-resolution Tampa Bay Coastal Ocean Model (TBCOM). Both models are configured to perform realistic simulations of ocean circulation and water levels which are then combined with tide gauge observations to provide 3-day hindcasts and 3.5-day forecasts of coastal water level along the West Florida coast (http://ocgweb.marine.usf.edu/Models/SeaLevel/). The experimental product was maintained during the approach and passage of Hurricanes Helene and Milton, and provided critical storm surge forecasts to a broad suite of stakeholders including the public. The system successfully predicted the water level set-up and set-down along the west Florida coast three days in advance of each hurricane, with improved forecasts realized each day prior to landfall. The TBCOM-inundation forecast system was also activated during Hurricane Helene. This modeling system extends its dense grid onto the land, facilitating simulation of inundation and flooding associated with storm surge in coastal areas. During Hurricane Helene, areas of severe inundation were identified along the coastal periphery of Tampa Bay and forecasts were accessible two days in advance of landfall.
Professor University of Memphis
A Scalable Framework for Multi-Hazard Flood Damage and Resilience Assessment in Urban Coastal Communities
Co-Author: Maryam Karimi
Abstract: Urban coastal regions are increasingly vulnerable to extreme flooding driven by hurricanes, sea-level rise, and compound coastal hazards, resulting in escalating economic losses, infrastructure damage, and threats to public safety. This study presents a comprehensive and scalable framework for quantifying community-scale flood damage and structural resilience across multiple spatial scales. Large-scale coastal flooding scenarios were simulated and validated using semi-coupled storm surge and two-dimensional inundation models. Key hydrodynamic parameters, including flood depth and flow momentum, were integrated into a newly developed multidimensional flood-damage assessment model.
The proposed framework extends traditional depth–damage relationships by incorporating building-specific characteristics such as height, age, configuration, and construction materials. Structural resilience was quantified as a function of flood-induced damage, recovery time, and community preparedness. Application of the framework to a highly vulnerable coastal study area revealed flood depths ranging from 4.91 m to 8.06 m under multiple hurricane categories, resulting in property damage levels of 28.69%, 45.62%, and 92.13% across scenarios.
Results were analyzed using geospatial techniques at both the property (microscale) and census block group (macroscale) levels, demonstrating the framework’s adaptability and scalability. By explicitly linking hydrodynamic processes with structural and community-level attributes, this work provides actionable insights to support hazard mitigation planning and enhance resilience in urban coastal systems facing recurrent extreme flooding.
Postdoctoral Scholar University of California, Berkeley
Multi-Hazard Resilience Assessment of Coastal Communities
Co-Author: Eyitayo Opabola (UC Berkeley)
Abstract: The United States’ coastal communities are increasingly exposed to extreme wind and flooding hazards. Although these hazards can occur independently, climate change is altering their intensity and frequency in ways that increase the likelihood of compound wind–flood events and amplify community-wide impacts. This study presents an integrated framework for multi-hazard resilience assessment of coastal communities under climate change. The framework generates climate-informed multi-hazard maps of wind speed and inundation depth at specified return periods for future time horizons under different climate scenarios, quantifies resulting community-level building damage and direct economic loss, and evaluates recovery trajectories using time-dependent restoration modeling and resilience metrics. The proposed framework enables climate-informed, community-level multi-hazard resilience assessment by linking future wind and inundation scenarios to a coastal community’s potential damage, loss, and recovery performance, while supporting consistent comparison between single-hazard and compound-hazard outcomes. A case study is conducted for a marginalized coastal community near San Francisco, California. Results show how climate change-driven shifts in hazard intensity and dependence influence spatial patterns of damage and loss and reshape recovery trajectories over time. The results provide actionable insights to prioritize mitigation measures, guide adaptation planning, and target resilience investments in coastal communities.