Machine learning–accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many physical
phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well …
phenomena, such as weather, climate, aerodynamics, and plasma physics. Fluids are well …
[HTML][HTML] Flood forecasting with machine learning models in an operational framework
Google's operational flood forecasting system was developed to provide accurate real-time
flood warnings to agencies and the public with a focus on riverine floods in large, gauged …
flood warnings to agencies and the public with a focus on riverine floods in large, gauged …
Detecting natural disasters, damage, and incidents in the wild
Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious
task performed by on-the-ground emergency responders and analysts. Social media has …
task performed by on-the-ground emergency responders and analysts. Social media has …
Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths
Flood inundation emulation models based on deep neural networks have been developed
to overcome the computational burden of two‐dimensional (2D) hydrodynamic models …
to overcome the computational burden of two‐dimensional (2D) hydrodynamic models …
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
E Weber, DP Papadopoulos… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the
Earth undergoes global warming. It is difficult to predict when and where an incident will …
Earth undergoes global warming. It is difficult to predict when and where an incident will …
Flood forecasting with machine learning models in an operational framework
The operational flood forecasting system by Google was developed to provide accurate real-
time flood warnings to agencies and the public, with a focus on riverine floods in large …
time flood warnings to agencies and the public, with a focus on riverine floods in large …
Multi-class segmentation under severe class imbalance: A case study in roof damage assessment
The task of roof damage classification and segmentation from overhead imagery presents
unique challenges. In this work we choose to address the challenge posed due to strong …
unique challenges. In this work we choose to address the challenge posed due to strong …
ML-based flood forecasting: Advances in scale, accuracy and reach
Floods are among the most common and deadly natural disasters in the world, and flood
warning systems have been shown to be effective in reducing harm. Yet the majority of the …
warning systems have been shown to be effective in reducing harm. Yet the majority of the …
[PDF][PDF] Flood forecasting with machine learning models in an operational framework
OG Reich, R Maor, S Timnat, T Shechter, V Anisimov… - scholar.archive.org
Google's operational flood forecasting system was developed to provide accurate real-time
flood warnings to agencies and the public, with a focus on riverine floods in large, gauged …
flood warnings to agencies and the public, with a focus on riverine floods in large, gauged …
[PDF][PDF] The Google Flood Forecasting Initiative
S Nevo - ai4sibook.org
The Google Flood Forecasting Initiative is the world's first large-scale machine-learning-
based operational flood forecasting system. It currently covers more than 360 million people …
based operational flood forecasting system. It currently covers more than 360 million people …