Machine learning for risk and resilience assessment in structural engineering: Progress and future trends
Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …
to increased exposure and vulnerability of structural and infrastructure systems to hazards …
Assessing landscape ecological vulnerability to riverbank erosion in the Middle Brahmaputra floodplains of Assam, India using machine learning algorithms
Riverbank erosion is one of the most catastrophic hazards that renders floodplains
vulnerable across the world vulnerable. It creates a significant negative impact on the …
vulnerable across the world vulnerable. It creates a significant negative impact on the …
Explainable deep learning powered building risk assessment model for proactive hurricane response
Climate change and rapid urban development have intensified the impact of hurricanes,
especially on the Southeastern Coasts of the United States. Localized and timely risk …
especially on the Southeastern Coasts of the United States. Localized and timely risk …
Social vulnerability and population loss in Puerto Rico after Hurricane Maria
J West - Population and Environment, 2023 - Springer
Abstract Communities in Puerto Rico saw their populations shrink after Hurricane Maria in
2017. Of the archipelago's 884 populated census tracts, 613 tracts (69.3%) experienced a …
2017. Of the archipelago's 884 populated census tracts, 613 tracts (69.3%) experienced a …
Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment
Coastal communities in various regions of the world are exposed to risk from tsunami
inundation, requiring reliable modeling tools for implementing effective disaster …
inundation, requiring reliable modeling tools for implementing effective disaster …
The importance of compounding threats to hurricane evacuation modeling
Climate change and the increasing complexity of society necessitate rethinking of siloed
threat scenarios in emergency response planning. Incorporating a compounding threat …
threat scenarios in emergency response planning. Incorporating a compounding threat …
Optimising hurricane shelter locations with smart predict-then-optimise framework
Hurricanes pose an escalating threat to global communities, underscoring the urgent need
for robust disaster response strategies. A pivotal component of these strategies involves the …
for robust disaster response strategies. A pivotal component of these strategies involves the …
A predictive assessment of households' risk against disasters caused by cold waves using machine learning
This paper trains a household-level disaster risk classifier based on supervised machine
learning algorithms for cold wave-related disasters. The households' features considered for …
learning algorithms for cold wave-related disasters. The households' features considered for …
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 …
will cause. We use random forest models fit to hazard, exposure, and vulnerability data at …
Towards rapid and automated vulnerability classification of concrete buildings
With the overwhelming number of older reinforced concrete buildings that need to be
assessed for seismic vulnerability in a city, local governments face the question of how to …
assessed for seismic vulnerability in a city, local governments face the question of how to …