Research

(1) City Digital Twin System for Coastal Flood Risk Assessment
This system will integrate extensive built environment data with an AI-based flood model to enhance situation assessment capabilities and facilitate informed long-term urban development decision-making. This system will provide broader impacts to society by giving decision-makers a comprehensive view of the potential consequences of flood scenarios and enabling them to evaluate, predict and guide the impact of urban development that can affect housing, jobs, and public services in cities, ultimately contributing to social equity.
Sponsor: Good Systems@UT-Austin

(2) Imputing Missing Structural Features using Machine Learning
Assessing building damage in coastal communities after a hurricane event is crucial for reducing both immediate and long-term disaster impacts, as well as for enhancing resilience planning and disaster preparedness. Despite the extensive data collection efforts of the post-hurricane reconnaissance teams, some information on the structural features of damaged buildings is often missing. This study introduces a machine-learning model that can reconstruct missing structural features of the damaged buildings from the reconnaissance datasets.

(3) Tsunami Machine Learning Model
We extend Lee et al.’s (2021) machine-learning model to facilitate rapid prediction of tsunami inundation from heterogeneous earthquake slip distributions. This model operates without the need for additional pre-calculated databases or observation data. This new model has the potential to enhance tsunami prediction capabilities, ultimately contributing to rapid tsunami forecasting and robust tsunami hazard assessment.

(1) Storm Surge Machine Learning Model 
​Storm surge by hurricane is a devastating threat to coastal communities in Virginia. Significant advances in physics-based models over the last several decades allow accurate simulation of storm surge at high resolution, and thus with high accuracy. The problem, however, is that a high-fidelity model like ADCIRC is computationally intensive. In this study, we developed a machine learning model that can rapidly predict the storm surge water level based on the pre-computed high-resolution dataset of the U.S. Army Corps of Engineers’ North Atlantic Comprehensive Coastal Study (NACCS) (Lee et al., 2021). 

(2) Data-Driven Tsunami Forward/Inverse Modeling
​A tsunami run-up response function (TRRF) is a data-driven model that can rapidly predict the near-field tsunami run-up distribution for any combination of fault parameters in real bathymetric and topographic conditions. The main idea of the TRRF is that the alongshore tsunami run-up distribution can be divided into source run-up (leading order contribution) and topographic run-up (residual part) terms, where the former is based on the Okal and Synolakis’ (2004) empirical formula. The results show that the TRRF is fast as an analytical approach (Computational time: < 1 sec) and as accurate as the numerical model (Lee et al., 2020, Lee et al., 2021, Lee et al., 2023).​

(3) Machine Learning-Based Real Time Water Level Gap Filling
Accurate prediction of missing water levels attributable to reasons ranging from recording failure and transmission problems to mistakes made by field staff is essential in coastal and oceanic areas. We developed a new system for data recovery based on the artificial neural network. The results indicate that although the performance of the proposed system declines marginally as the gap size increases, it performs creditably in alleviating the gap-filling problem (Lee and Park, 2016).