A Hybrid Spatial Decision Support System for Street Level Flood Mapping and Emergency Management

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
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: 4/10/2021
Start / End Time: 4:40 PM / 5:55 PM
Room: Virtual 9
Presentation File: No File Uploaded

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.

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