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Machine Learning. Earth

IOP Publishing · United Kingdom

eISSN3049-4753

✓ DOAJ✓ Open Access

Aims & Scope✦ Inferred from recent articles

This journal focuses on the application of machine learning and artificial intelligence techniques to address challenges in Earth sciences. Articles explore the use of these methods for improving predictions and understanding in areas such as soil moisture, earthquake and volcano monitoring, climate modeling, wildfire danger assessment, streamflow forecasting, weather prediction, seismic imaging, precipitation phase determination, climate projections, low-level cloud field analysis, wind speed estimation, and forest structural complexity mapping. The research often involves developing novel deep learning architectures, utilizing foundation models, and applying explainable AI for interpretability and uncertainty quantification.

AI-summarised from recent articles · verify on publisher page →

General Information

Country / RegionUnited Kingdom

Primary LanguageEnglish

1st Year Published—

StatusActive

Total Publications—

Visit Journal Website

Submission Info

Peer ReviewAnonymous peer review, Double anonymous peer review

OA LicenseCC BY

OA Rate—

Ethics & Quality

COPE Member✗ No

OASPA Member✗ No

Not on Predatory Lists✓ Yes

Think.Check.Submit Compliance

thinkchecksubmit.org

8/11 · 73%

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Do you know the journal / publisher?

IOP Publishing

✅

Does the journal have a website?

✓ Linked

✅

Is the ISSN verified?

3049-4753

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Indexed in a trusted database?

DOAJ

✅

Peer review process documented?

Anonymous peer review, Double anonymous peer review

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Follows ethical publishing standards (COPE)?

N/A

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Not on predatory/blacklists?

✓ Clean

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Long-term digital preservation?

N/A

❌

Plagiarism detection in place?

N/A

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Listed in DOAJ (verified OA)?

DOAJ verified

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Primary language documented?

English

A twelfth criterion — whether APC fees are clearly disclosed — is not scored here; it is left out of the total rather than counted as a failure. Publication charges appear in the metrics card above.

Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.

Subject Classification

Research Topics (OpenAlex)

Meteorological Phenomena and SimulationsHydrological Forecasting Using AIClimate variability and modelsModel Reduction and Neural NetworksOceanographic and Atmospheric ProcessesFluid Dynamics and Turbulent FlowsFire effects on ecosystemsPrecipitation Measurement and AnalysisTropical and Extratropical Cyclones ResearchHydrology and Watershed Management Studies

Frequently asked questions about Machine Learning. Earth

Is Machine Learning. Earth a predatory journal?

PubScope has no integrity flags on record for Machine Learning. Earth: it is indexed in DOAJ, and is not on DOAJ's withdrawn list. Indexing is a transparency signal, not a guarantee — always confirm fit and policies before submitting.

What is the impact factor of Machine Learning. Earth?

Machine Learning. Earth is not in the Web of Science Core Collection, so it has no official Clarivate Journal Impact Factor.

Is Machine Learning. Earth indexed in Scopus and Web of Science?

Machine Learning. Earth is indexed in DOAJ.