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machine learning & AI

Artistic impression of artificial intelligence
Posted inEditors' Vox

Tackling 21st Century Geoscience Problems with Machine Learning

by A. Curtis, D. O'Malley, G. C. Beroza, P. A. Johnson and E. Li 7 October 202013 October 2021

A new cross-journal special collection invites contributions on how machine learning can be used for solid Earth observation, modeling and understanding.

Diagram showing sea surface temperature (SST) anomalies in February 1987
Posted inResearch Spotlights

Interpreting Neural Networks’ Reasoning

Kate Wheeling, freelance science writer by Kate Wheeling 2 September 20206 June 2022

New methods that help researchers understand the decision-making processes of neural networks could make the machine learning tool more applicable for the geosciences.

Map of Land subsidence predictions in the western United States obtained via machine learning
Posted inEditors' Highlights

Machine Learning Predicts Subsidence from Groundwater Pumping

by Marc F. P. Bierkens 17 August 202031 March 2023

Machine learning and data on aquifer type, sediment thickness, and proxies for irrigation water use has been used to produce the most comprehensive map of land subsidence in the western U.S. to date.

The central processing unit–based Cheyenne supercomputer at the National Center for Atmospheric Research (NCAR)–Wyoming Supercomputing Center
Posted inOpinions

Earth System Modeling Must Become More Energy Efficient

by R. Loft 28 July 202019 August 2022

As weather and climate models grow larger and more data intensive, the amount of energy needed to run them continues to increase. Are researchers doing enough to minimize the carbon footprint of their computing?

A home severely damaged by a tornado
Posted inOpinions

Weathering Environmental Change Through Advances in AI

by Amy McGovern, A. Bostrom, I. Ebert-Uphoff, R. He, C. Thorncroft, P. Tissot, S. Boukabara, J. Demuth, D. J. Gagne II, J. Hickey and J. K. Williams 28 July 202022 November 2021

Developing trustworthy artificial intelligence for weather and ocean forecasting, as well as for long-term environmental sustainability, requires integrating collaborative efforts from many sources.

An abstract illustration showing many points of data being sorted along streams.
Posted inAGU News

The Rise of Machine Learning

Heather Goss, AGU Publisher by Heather Goss 28 July 202019 October 2022

Our August issue explores the way we process, analyze, and clearly present the massive amounts of information collected by scientists today.

Map of the Gusev Crater region of Mars with craters detected by an algorithm shown in red
Posted inEditors' Highlights

Who Wants to Count All the Craters on Mars? Not Me!

by P. Fox 21 July 202028 January 2022

Humans found hundreds of thousands of craters on Mars greater than 1 kilometer in diameter, but now computers automate the process delivering crater counts as well as geologically meaningful ages.

Charts comparing the Standardized Precipitation-Evapotranspiration Index and the standardized simulations at multiple timescales for the study area, which is shown on a map on the left
Posted inEditors' Highlights

Ensemble Learning Estimates Terrestrial Water Storage Changes

by Jonathan H. Jiang 2 July 20206 February 2023

Ensemble learning models for estimating past changes of terrestrial water storage from climate are presented and tested in the Pearl River basin, China.

Painting called "The False Mirror" by Rene Magritte
Posted inEditors' Highlights

The Perils of Computing Too Much and Thinking Too Little

by B. Stevens 25 June 202021 September 2022

Big steps forward are synonymous with new ideas – a thought that merits mindfulness if we aspire to train students to do more than merely train machines.

Illustration of a blue planet with a network of data connections and computer code in its atmosphere
Posted inNews

Machine Learning Can Help Decode Alien Skies—Up to a Point

Kimberly M. S. Cartier, News Writing and Production Intern for Eos.org by Kimberly M. S. Cartier 25 June 202016 June 2022

Astronomers are testing the tools that might help them keep up with the upcoming storm of exoplanet atmosphere data.

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