8 citations · 12 across the 4 of their papers we have counts for
5 papers
Earthquake magnitudes depend on seismic history, as revealed by a neural network analysis
Neri Berman, Oleg Zlydenko, Oren Gilon +2
Earthquake occurrence is notoriously difficult to predict. While some aspects of their spatiotemporal statistics can be relatively well captured by point-process models, very littl…
AI Increases Global Access to Reliable Flood Forecasts
Grey Nearing, Deborah Cohen, Vusumuzi Dube +15
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely war…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
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,…
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
Sella Nevo, Gal Elidan, Avinatan Hassidim +4
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 worl…
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy
Genady Beryozkin, Yoel Drori, Oren Gilon +2
We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identi…