A Hybrid Spatial Decision Support System for Street Level Flood Mapping and Emergency Management
Topics: Geographic Information Science and Systems
, Hazards, Risks, and Disasters
, Water Resources and Hydrology
Keywords: Flooding mitigation, Emergency Management, GIS and Spatial Decision Support Systems, Citizen Science, GeoAI
Session Type: Virtual Paper
Day: Saturday
Session Start / End Time: 4/10/2021 04:40 PM (Pacific Time (US & Canada)) - 4/10/2021 05:55 PM (Pacific Time (US & Canada))
Room: Virtual 9
Authors:
Zhe Zhang, Texas A&M University
Amir Behzadan, Department of Construction Science, Texas A&M University
Michelle Meyer, Texas A&M Hazard Reduction and Recovery Center, Texas A&M University
Courtney Thompson, Department of Geography, Texas A&M University
Bahareh Kharazi, Department of Construction Science, Texas A&M University
Diya Li, Department of Geography, Texas A&M University
Julia Hillin, Department of Geography, Texas A&M University
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Abstract
In the United States, almost 40 percent of the population lives in relatively high population-density coastal areas, where water-related hazards such as hurricanes and floods happen more frequently. For instance, Hurricane Harvey damaged more than 100,000 households and resulted in 68 death. Therefore, a significant need exists to establish an informative and robust community-scale flood adaptation system that can help coastal communities to mitigate better, prepare for, respond to, and recover from water-related disasters. This research project introduces a citizen science-driven flood depth mapping tool called BluPix, which can help communities better document and understand flood risk at a fine spatiotemporal scale. Our models compare pre-flood and post-flood photos of the same location to estimate floodwater depth in that particular location. We use traffic signs as benchmarks since their shapes and sizes are standardized anywhere in the country. After that, we will introduce an interactive spatial decision support tool that can help a user prioritize different decision choices (e.g., evacuation routes) to optimize flood emergency management.
