A5059765585- Flood Risk Assessment and Management
- Hydrology and Drought Analysis
- Disaster Management and Resilience
- Landslides and related hazards
- Tropical and Extratropical Cyclones Research
- Infrastructure Resilience and Vulnerability Analysis
- Evacuation and Crowd Dynamics
Texas A&M University at Galveston
2020-2024
Abstract. Pre-disaster planning and mitigation necessitate detailed spatial information about flood hazards their associated risks. In the US, Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) provides important areas subject to flooding during 1 % riverine or coastal event. The binary nature of hazard maps obscures distribution property risk inside SFHA residual outside SFHA, which can undermine efforts. Machine learning techniques provide an alternative approach...
<title>Abstract</title> Exploring the intricate dynamics between vulnerability of built environment and resilience businesses is not only pivotal for equitable recovery resource allocation, especially among underprivileged populations but also remains a relatively unexplored area within realm urban studies. This study uses fine-resolution human mobility property-level flood damage claim data to uncover rather unknown effects built-environment on local economies. Using from 2017 Hurricane...
Flooding is increasing worldwide, and many current maps models available to help people understand their risk are not designed with communication best practices in mind. This study one of the first combine comparative U.S. flood modelling approaches measures perceived map usability accuracy begin how may be used communicate risk. Using a survey residents Texas, counties affected by 2017 event (N = 404), this captures perceptions two different types three legends. Findings suggest that even...
Abstract. Pre-disaster planning and mitigation necessitates detailed spatial information about flood hazards their associated risks. In the U.S., FEMA Special Flood Hazard Area (SFHA) provides important areas subject to flooding during 1 % riverine or coastal event. The binary nature of hazard maps obscures distribution property risk inside SFHA residual outside SFHA, which can undermine efforts. Machine-learning techniques provide an alternative approach estimating across large scales at...