Ph.D. Thesis Defense Announcement

MULTI-MODAL DATA-DRIVEN APPROACHES FOR DISASTER DAMAGE ASSESSMENT AND PREDICTION

By Danrong Zhang

Advisor:

Dr. J. David Frost (CEE) & Dr. Nimisha Roy (SCI)

Committee Members: 

Dr. Yi-Chang James Tsai (CEE)

Dr. Duen Horng (Polo) Chau (CSE)

Dr. M. Mahdi Roozbahani (SCI)

Date and Time: October 31, 2024. 12:00pm EST

Location: SEB 122

As climate change accelerates, disasters pose an increasing threat to human lives
and infrastructure. Disaster management is evolving from a reactive approach—
addressing damage only after it occurs—to a proactive stage, where potential
disasters are anticipated and preparations are made, and ultimately to a predictive
stage, where data is used to forecast disaster impacts. While current disaster
response remains largely reactive, with growing efforts towards proactive measures,
this work addresses the gaps in reactive post-disaster damage assessments and
advances the field towards proactive and predictive disaster management, with the
goal of improving overall preparedness.
This research employs multi-modal data-driven methods to enhance both postdisaster
damage assessment and pre-disaster damage prediction. By integrating Geographic Information Systems (GIS), data analytics, and machine learning with
diverse data, such as tabular data, social media imagery, satellite imagery, and
nighttime light data, the study provides critical insights into disasters like hurricanes,
tornadoes, earthquakes, and landslides. These approaches equip stakeholders with
valuable information, reinforcing disaster preparedness and response strategies to
mitigate future risks and enhance community resilience.