Building Classification Using Random Forest to Develop a Geodatabase for Probabilistic Hazard Information
Bibliographic Data
| ID | 21738411 |
|---|---|
| Authors | Jooho Kim (0000-0002-0395-5107, Cooperative Institute for Mesoscale Meteorological Studies, corresponding author), Joshua J Hatzis (0000-0002-8291-6551, Cooperative Institute for Mesoscale Meteorological Studies), Kim Klockow (NOAA National Severe Storms Laboratory), Patrick A Campbell (0000-0001-8571-9222, NOAA National Severe Storms Laboratory) |
| Year | 2022 |
| Volume | 23 |
| Issue | 3 |
| Publication date | 2022-08-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Natural Hazards Review (JOURNAL) |
| Journal identifiers | ISSN: 1527-6988 • E-ISSN: 1527-6996 |
| Publisher | American Society of Civil Engineers (ASCE) (PUBLISHER • US) |
| DOI | 10.1061/(asce)nh.1527-6996.0000561 |
| OpenAlex | W4281659918 |
| Language | EN |
| Citations received | 3 |
| References cited | 56 |
To understand the community risk from severe weather threats, two components, including weather information and community assets, are crucial. Recently, probabilistic hazard information (PHI) from the National Oceanic and Atmospheric Administration (NOAA) Forecasting a Continuum of Environmental Threats (FACETs) program has been developed to provide dynamic weather-related information between the watch and warning systems to weather forecasters, emergency management agencies, and the public. To predict community physical risks on critical infrastructure and building properties using PHI, building type information is required. This study applied a machine learning technique to predict building types using building footprint and city zoning data. We collected Oklahoma county building property data to train and test a random forest model. The result of this study showed that building footprint and city zoning data can be applied to classify multiple building types with an accuracy of 96%. The machine learning–based building classification contributed to the acquisition of building type data in the Oklahoma City, Oklahoma, metropolitan area. This geodatabase will be utilized to predict real-time critical infrastructure and building damage assessment using PHI. In addition to their importance to physical building damage assessment, the results can be utilized to develop postdisaster responses and planning
Built environment · Civil engineering · Emergency management · Environmental resource management · Footprint · Geographic information system · Geography · Hazard · Metropolitan area · Remote sensing · Transport engineering · Warning system · Zoning · Computer Science · Data-Driven Disease Surveillance · Engineering · Environmental Science · Infrastructure Maintenance and Monitoring · Traffic Prediction and Management Techniques
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |