The new graph convolutional recurrent neural network (GCRNN) will enable water utilities to forecast water use, even if some sensors fail.
machine learning & AI
Posted inEditors' Highlights
Modeling Entrainment with Machine Learning
Researchers present a new approach to modeling the stochastic mixing process of convection using a machine learning technique.
Posted inFeatures
Fiona Lo: A “Really Long, Convoluted Path” to Health
Lo uses her background in atmospheric sciences to forecast pollen concentrations.
Posted inScience Updates
For Western Wildfires, the Immediate Past Is Prologue
A new machine learning approach trained on winter and spring climate conditions offers improved forecasts of summer fire activity across the western United States.
Posted inEditors' Vox
Dynamics of Volcanic Processes
A new cross-journal special collection invites contributions on modern approaches used to investigate dynamics of volcanic processes.
Posted inNews
Monitoreando terremotos a la velocidad de la luz
Nueva investigación utiliza la gravedad y un modelo de aprendizaje automático para estimar instantáneamente la magnitud y ubicación de grandes terremotos.
Posted inResearch Spotlights
Despite Improvements, China’s Air Remains Unsafe
Toxic particulate matter has decreased by about a third over the past decade, but levels are still above what’s considered healthy.
Posted inEditors' Highlights
Learning from Climate Simulations for Global Seasonal Forecast
A probabilistic deep learning methodology that learns from climate simulation big data offers advantageous seasonal forecasting skill and crucial climate model diagnosis information at a global scale.
Posted inResearch Spotlights
Algorithm Detects Thousands of Missing Levees from U.S. Database
An existing levee database accounts for just one fifth of the country’s actual total levee count, limiting the study of how these embankments affect riparian ecosystem health in the United States.
Posted inEditors' Vox
The Big Data Revolution Unlocks New Opportunities for Seismology
The field of seismology is entering a new era where our understanding of earthquakes and the solid earth is increasingly driven by new Big Data experiments and algorithms.