Katherine A Serafin
Datos Biográficos
| ID | 7992208 |
|---|---|
| NOMBRE | Katherine A Serafin |
| NOMBRES | Katherine A |
| APELLIDO | Serafin |
| FIRMA | SERAFIN K A |
| AFILIACIONES | University of Florida |
| ORCID | 0000-0002-4127-9787 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2024 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2024 |
| ÍNDICE H | 0 |
Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning
The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretabl…
Moving from total risk to community-based risk trajectories increases transparency and equity in flood risk mitigation planning along urban rivers
After several years of drought, 2023 and early 2024 are reminders of the powers of California’s atmospheric rivers and the devastating flooding they can entail. Aged flood-mitigation infrastructure and climate change exacerbate flood risk for some communities more than for others, highlighting the challenge of equitably mitigating flood risk. Identifying inequities associated with infrastructure projects is now legally required by regional water …
Sin obras prominentes en esta página.
Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning
The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretabl…
Moving from total risk to community-based risk trajectories increases transparency and equity in flood risk mitigation planning along urban rivers
After several years of drought, 2023 and early 2024 are reminders of the powers of California’s atmospheric rivers and the devastating flooding they can entail. Aged flood-mitigation infrastructure and climate change exacerbate flood risk for some communities more than for others, highlighting the challenge of equitably mitigating flood risk. Identifying inequities associated with infrastructure projects is now legally required by regional water …
Computer Science (2 obras) · Environmental resource management (2 obras) · Environmental Science (2 obras) · Flood myth (2 obras) · Flood Risk Assessment and Management (2 obras) · Geography (2 obras) · Business (1 obras) · Computer security (1 obras) · Engineering (1 obras) · Environmental planning (1 obras)