School of Civil and Environmental Engineering

Ph.D. Thesis Defense Announcement

MODELING URBAN FLOOD RISKS

FOR RESILIENT INFRASTRUCTURE, EMERGENCY SERVICES,

AND PUBLIC SAFETY

By Xiyu Pan

Advisor:

Dr. John E. Taylor

Committee Members:  Dr. Jian Luo (CEE); Dr. Yi-Chang James Tsai (CEE); Dr. Yiyi He (CRP); Dr. Neda Mohammadi (CEE)

Date and Time:  Nov 21, 2025, 1PM-3PM

Location: SEB 122

Urban flooding poses severe threats to Emergency Medical Services (EMS), with response times increasing by an average of 25% during flood events and up to tenfold in extreme cases. This dissertation develops a comprehensive framework for estimating and mitigating EMS delay risks during urban floods through advanced flood situation awareness and traffic disruption modeling. The research addresses critical gaps in current methodologies by integrating real-time sensing, data-driven modeling, and risk assessment to support dynamic EMS resource management. The first study establishes a framework for EMS delay risk estimation during urban floods, revealing significant inaccuracies and inequities in state-of-the-art methods when evaluated against real-world EMS data. The analysis demonstrates that lower-income communities not only experience disproportionately greater impacts from urban flooding, but also facing more flood risk underestimation, highlighting the need for more equitable risk assessment approaches. The second study addresses the data quality challenges in flood modeling by developing an error correction method for AI-based river streamflow forecasting. This method reduces the impact of measurement inaccuracies during flood events, improving the reliability of hydrological predictions that serve as critical inputs for urban flood modeling and subsequent EMS risk assessments. The third study introduces a novel application of roadside flood sensors for real-time traffic state estimation during floods. Using a double-layered Bayesian Network framework, this research develops an optimization approach for sensor positioning that maximizes the value of information for traffic disruption assessment, accounting for probabilistic dependencies and uncertainties across connected road networks. Collectively, these studies contribute to building a dynamic, real-time EMS delay risk estimation and mitigation system. The integrated framework supports proactive decision-making for emergency response operations, including ambulance deployment optimization and critical infrastructure recovery prioritization, ultimately enhancing community resilience to urban flooding disasters.