Joshua J Hatzis
Biographic Data
| ID | 9720516 |
|---|---|
| NAME | Joshua J Hatzis |
| GIVEN NAMES | Joshua J |
| FAMILY NAME | Hatzis |
| SIGNATURE | HATZIS J J |
| AFFILIATIONS | University of Wisconsin–Milwaukee |
| ORCID | 0000-0002-8291-6551 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
CNN-Based Building Type Classification into EF Scale Categories to Support Tornado Damage Assessments
Accurate assessment of tornado impacts requires detailed information on building characteristics that influence vulnerability. This study investigates a convolutional neural network (CNN)-based approach to classify nonresidential buildings into enhanced Fujita (EF) damage categories using image data. Three deep learning architectures—CNN, ConvNeXt, and ResNet50—were evaluated on a data set representing 11 EF building categories. ResNet50 achieved…
An Agent-Based Modeling Approach to Protective Action Decision-Related Travel during Tornado Warnings
Tornadoes represent a significant threat to life and property and tend to evoke protective action in most people. Studies have suggested that many people travel to the nearest storm shelter or flee the area, rather than sheltering-in-place as recommended by the National Weather Service (NWS). While shelter-in-place is the recommendation of the NWS, for tornado safety, few studies have quantified the risk reduction when compared to traveling to a …
A Spatiotemporal Perspective on the 31 May 2013 Tornado Evacuation in the Oklahoma City Metropolitan Area
On 31 May 2013, an extremely large and violent tornado hit near the town of El Reno, Oklahoma, a small town in the Oklahoma City metropolitan area. The size and intensity of this tornado, coupled with the fact that it was heading toward Oklahoma City, prompted local broadcasters to warn residents to evacuate their homes and head south if they could not shelter belowground. This warning led to a large-scale evacuation of the metropolitan area and …
Building Classification Using Random Forest to Develop a Geodatabase for Probabilistic Hazard Information
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, …
Spatiotemporal Analysis of Near-Miss Violent Tornadoes in the United States
In the hazards literature, a near-miss is defined as an event that had a nontrivial probability of causing loss of life or property but did not due to chance. Frequent near-misses can desensitize the public to tornado risk and reduce responses to warnings. Violent tornadoes rarely hit densely populated areas, but when they do they can cause substantial loss of life. It is unknown how frequently violent tornadoes narrowly miss a populated area. To…
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Spatiotemporal Analysis of Near-Miss Violent Tornadoes in the United States
In the hazards literature, a near-miss is defined as an event that had a nontrivial probability of causing loss of life or property but did not due to chance. Frequent near-misses can desensitize the public to tornado risk and reduce responses to warnings. Violent tornadoes rarely hit densely populated areas, but when they do they can cause substantial loss of life. It is unknown how frequently violent tornadoes narrowly miss a populated area. To…
A Spatiotemporal Perspective on the 31 May 2013 Tornado Evacuation in the Oklahoma City Metropolitan Area
On 31 May 2013, an extremely large and violent tornado hit near the town of El Reno, Oklahoma, a small town in the Oklahoma City metropolitan area. The size and intensity of this tornado, coupled with the fact that it was heading toward Oklahoma City, prompted local broadcasters to warn residents to evacuate their homes and head south if they could not shelter belowground. This warning led to a large-scale evacuation of the metropolitan area and …
Building Classification Using Random Forest to Develop a Geodatabase for Probabilistic Hazard Information
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, …
An Agent-Based Modeling Approach to Protective Action Decision-Related Travel during Tornado Warnings
Tornadoes represent a significant threat to life and property and tend to evoke protective action in most people. Studies have suggested that many people travel to the nearest storm shelter or flee the area, rather than sheltering-in-place as recommended by the National Weather Service (NWS). While shelter-in-place is the recommendation of the NWS, for tornado safety, few studies have quantified the risk reduction when compared to traveling to a …
CNN-Based Building Type Classification into EF Scale Categories to Support Tornado Damage Assessments
Accurate assessment of tornado impacts requires detailed information on building characteristics that influence vulnerability. This study investigates a convolutional neural network (CNN)-based approach to classify nonresidential buildings into enhanced Fujita (EF) damage categories using image data. Three deep learning architectures—CNN, ConvNeXt, and ResNet50—were evaluated on a data set representing 11 EF building categories. ResNet50 achieved…
Geography (4 works) · Tornado (4 works) · Meteorology (3 works) · Archaeology (2 works) · Computer Science (2 works) · Engineering (2 works) · Environmental Science (2 works) · Flood Risk Assessment and Management (2 works) · Footprint (2 works) · Hazard (2 works)