Building Classification Using Random Forest to Develop a Geodatabase for Probabilistic Hazard Information
Publication: Natural Hazards Review
Volume 23, Issue 3
Abstract
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.
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Data Availability Statement
The following data sets are available from the author ([email protected]) upon reasonable request:
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building footprint,
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city zoning data, and
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building property data.
Acknowledgments
Funding was provided by the National Oceanic and Atmospheric Administration (NOAA) Office of Oceanic and Atmospheric Research (OAR) and Office of Weather and Air Quality (OWAQ) (Federal Grant No. NA18OAR4590386—Implementing convective storm statistics from a large reanalysis of WSR-88D data for model verification and forecasting probabilistic uncertainty).
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Natural Hazards Review
Volume 23 • Issue 3 • August 2022
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© 2022 American Society of Civil Engineers.
History
Received: Jul 12, 2021
Accepted: Feb 2, 2022
Published online: Apr 6, 2022
Published in print: Aug 1, 2022
Discussion open until: Sep 6, 2022
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