ISSN: 3049-4753
Machine Learning: Earth is a multidisciplinary open access journal dedicated to the application of machine learning, artificial intelligence (AI) and data-driven computational methods across all areas of Earth, environmental and climate sciences including efforts to ensure a sustainable future. The journal publishes research reporting data-driven approaches that advance our knowledge of the Earth system, and of the interactions between biosphere, hydrosphere, cryosphere, atmosphere and geosphere. The journal also publishes research that presents methodological, theoretical, or conceptual advances in machine learning and AI with applications to Earth, environmental and climate science.
Free for readers. All article publication charges currently paid by IOP Publishing.
"Machine learning and artificial intelligence (AI) are revolutionizing the way we understand and can predict the Earth system. As our planet faces increasing environmental challenges—ranging from climate change to biodiversity loss and resource depletion—data-driven methods provide powerful tools to better monitor, innovate and drive solutions across the Earth and environmental sciences. Machine Learning: Earth serves as a platform for advancing interdisciplinary research that bridges AI and Earth system science. The journal focuses on innovative approaches that enhance our ability to model, predict, and manage the complex interactions between the atmosphere, oceans, biosphere, hydrosphere, cryosphere, and geosphere. Machine Learning: Earth welcomes applications of AI to the Earth system but also innovations on the algorithmic part. By fostering cross-disciplinary approaches, this journal aims to accelerate scientific discoveries that support a sustainable and resilient future for our planet." Pierre Gentine, Editor-in-Chief, Columbia University, USA
Why should you publish in Machine Learning: Earth?
Flexible and innovative article formats: In addition to Research articles and Reviews, you can publish Dataset articles, Benchmarks and Challenges, recognising the diverse publishing needs of researchers working at the interface of machine learning and AI across the sciences.
Open access: your paper will be published under a CC BY licence, enabling immediate and perpetual access, and permitting the widest possible dissemination and reuse of your research.
High quality peer review: all articles will be rigorously peer reviewed by IOP Publishing's global network of expert reviewers, supported by our top-level Editorial Board.
Fast publication: we are committed to providing you with a fast, professional service to ensure rapid first decision, acceptance and publication. Once accepted, your article will be accessible to readers within 24 hours and will include a citable DOI.
Pre-print friendly: you are encouraged to post on community pre-print platforms.
Data and code: research published in Machine Learning: Earth can include citable datasets and programmable code.
Society-owned: IOP Publishing is a leading society publisher of advanced physics research. Any profits generated by IOP Publishing are invested in the Institute of Physics, helping to support research, education and outreach around the world.
Part of our Machine Learning series™: The world's first open access journal series dedicated to the application and development of machine learning and AI across the sciences.