Journal of Geophysical Research: Machine Learning and Computation- Impact Score, Ranking, SJR, h-index, Citescore, Rating, Publisher, ISSN, and Other Important Details

About Journal of Geophysical Research: Machine Learning and Computation

Journal of Geophysical Research: Machine Learning and Computation is a journal published by John Wiley and Sons Inc. This journal covers the area[s] related to Chemical Engineering (miscellaneous), Civil and Structural Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Management of Technology and Innovation, etc. The coverage history of this journal is as follows: 2024-2026. The rank of this journal is 7242. This journal's impact score, h-index, and SJR are 3.63, 8, and 0.773, respectively. The ISSN of this journal is/are as follows: 29935210.

The best quartile of Journal of Geophysical Research: Machine Learning and Computation is Q1. This journal has received a total of 290 citations during the last three years (Preceding 2025).

Journal of Geophysical Research: Machine Learning and Computation Impact Score 2025-2026

The latest impact score (IS) of the Journal of Geophysical Research: Machine Learning and Computation is 3.63. It is computed in the year 2026 as per its definition and based on Scopus data.

The impact score (IS), also denoted as the Journal impact score (JIS), of an academic journal is a measure of the yearly average number of citations to recent articles published in that journal. It is based on Scopus data.

Prediction of Journal of Geophysical Research: Machine Learning and Computation Impact Score 2026

Impact Score 2025 of Journal of Geophysical Research: Machine Learning and Computation is 3.63. If a similar upward trend continues, IS may increase in 2026 as well.

Journal of Geophysical Research: Machine Learning and Computation h-index

 

The h-index of Journal of Geophysical Research: Machine Learning and Computation is 8. By definition of the h-index, this journal has at least 8 published articles with more than 8 citations.

What is h-index?

The h-index (also known as the Hirsch index or Hirsh index) is a scientometric parameter used to evaluate the scientific impact of the publications and journals. It is defined as the maximum value of h such that the given Journal has published at least h papers and each has at least h citations.

Journal of Geophysical Research: Machine Learning and Computation ISSN

The International Standard Serial Number (ISSN) of Journal of Geophysical Research: Machine Learning and Computation is/are as follows: 29935210.

The ISSN is a unique 8-digit identifier for a specific publication like Magazine or Journal. The ISSN is used in the postal system and in the publishing world to identify the articles that are published in journals, magazines, newsletters, etc. This is the number assigned to your article by the publisher, and it is the one you will use to reference your article within the library catalogues.

ISSN code (also called as "ISSN structure" or "ISSN syntax") can be expressed as follows: NNNN-NNNC
Here, N is in the set {0,1,2,3...,9}, a digit character, and C is in {0,1,2,3,...,9,X}

Journal of Geophysical Research: Machine Learning and Computation Ranking and SCImago Journal Rank (SJR)

The Journal of Geophysical Research: Machine Learning and Computation is ranked 7242 among 27955 Journals, Conferences, and Book Series. As per SJR, this journal is ranked 0.773.

SCImago Journal Rank is an indicator, which measures the scientific influence of journals. It considers the number of citations received by a journal and the importance of the journals from where these citations come.

Journal of Geophysical Research: Machine Learning and Computation Publisher

The publisher of Journal of Geophysical Research: Machine Learning and Computation is John Wiley and Sons Inc. The publishing house of this journal is located in the United States. Its coverage history is as follows: 2024-2026.

Call For Papers (CFPs)

Please check the official website of this journal to find out the complete details and Call For Papers (CFPs).

Abbreviation

The International Organization for Standardization 4 (ISO 4) abbreviation of Journal of Geophysical Research: Machine Learning and Computation is J. Geophys. Res.: Mach. Learn. Comput.. ISO 4 is an international standard which defines a uniform and consistent system for the abbreviation of serial publication titles, which are published regularly. The primary use of ISO 4 is to abbreviate or shorten the names of scientific journals using the technique of List of Title Word Abbreviations (LTWA).

As ISO 4 is an international standard, the abbreviation ('J. Geophys. Res.: Mach. Learn. Comput.') can be used for citing, indexing, abstraction, and referencing purposes.

How to publish in Journal of Geophysical Research: Machine Learning and Computation

If your area of research or discipline is related to Chemical Engineering (miscellaneous), Civil and Structural Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Management of Technology and Innovation, etc., please check the journal's official website to understand the complete publication process.

Acceptance Rate

There are several factors that influence the acceptance rate of any academic journal. Here are some critical parameters that are considered while calculating the acceptance rate:

• Interest/demand of researchers/scientists for publishing in a specific journal/conference.
• The complexity of the peer review process and timeline.
• Time taken from draft submission to final publication.
• Number of submissions received and acceptance slots
• And Many More.

The simplest way to find out the acceptance rate or rejection rate of a Journal/Conference is to check with the journal's/conference's editorial team through emails or through the official website.

Credits and Sources

• Scimago Journal & Country Rank (SJR), https://www.scimagojr.com/
• Journal Impact Factor, https://clarivate.com/
• Issn.org, https://www.issn.org/
• Scopus, https://www.scopus.com/