Atieh Alipour
Biographic Data
| ID | 7999726 |
|---|---|
| NAME | Atieh Alipour |
| GIVEN NAMES | Atieh |
| FAMILY NAME | Alipour |
| SIGNATURE | ALIPOUR A |
| AFFILIATIONS | University of Alabama |
| ORCID | 0000-0001-5058-9173 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Leveraging machine learning for predicting flash flood damage in the Southeast US
Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framewor…
Toward a more effective hurricane hazard communication
Tropical cyclones are among the most devastating natural disasters that pose risk to people and assets all around the globe. The Saffir-Simpson scale is commonly used to inform threatened communities about the severity of hazard, but lacks consideration of other potential drivers of a hazardous situation (e.g. terrestrial and coastal flooding). Here, we propose an alternative approach that accounts for multiple components and their likelihood of …
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Leveraging machine learning for predicting flash flood damage in the Southeast US
Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framewor…
Toward a more effective hurricane hazard communication
Tropical cyclones are among the most devastating natural disasters that pose risk to people and assets all around the globe. The Saffir-Simpson scale is commonly used to inform threatened communities about the severity of hazard, but lacks consideration of other potential drivers of a hazardous situation (e.g. terrestrial and coastal flooding). Here, we propose an alternative approach that accounts for multiple components and their likelihood of …
Computer Science (2 works) · Environmental Science (2 works) · Flood Risk Assessment and Management (2 works) · Geography (2 works) · Hazard (2 works) · Meteorology (2 works) · Natural hazard (2 works) · Tropical and Extratropical Cyclones Research (2 works) · Artificial Intelligence (1 works) · Atlantic hurricane (1 works)