Recently emerging trends in big data analytic methods for modeling and combating climate change effects
Open accessAbstract
Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals.
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In assessment and grading, XAI enhances objectivity and fairness by providing transparent insights into grading criteria and performance metrics, enabling educators to make informed evaluations and provide constructive feedback.
“In assessment and grading, XAI enhances objectivity and fairness by providing transparent insights into grading criteria and performance metrics, enabling educators to make informed evaluations and provide constructive feedback [67,69].”
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SELECTION OF DATABASES TO STORE GEOSPATIAL-TEMPORAL DATA
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Synoptic-Scale Wildland Fire Weather Conditions in Mexico
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“Why nature won’t save us from climate change but technology will”:
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Flagged first, then by citations
Research works printed but not linked: 11
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- Hua-dong G, Li Z, Lan-wei Z (2015) Earth observation technology has provided highly useful information in global. Adv Clim Change Res. https:// doi. org/ 10. 1016/j. accre. 2015. 09. 007. · DOI
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Cited by
Sorted by citations
Mathew, D. E., Ebem, D. U., Ikegwu, A. C., Ukeoma, P. E., & Dibiaezue, N. F. (2025) · Neural Processing Letters · doi:10.1007/s11063-025-11732-2 · Cited by 168 · see citing works
Self-citation
- Authors
- Ikegwu, A. C., Nweke, H. F., Mkpojiogu, E., Anikwe, C. V., Igwe, S. A., & Alo, U. R.
- Affiliations
- Veritas University (NG)
- David Umahi Federal University of Health Sciences Uburu (NG)
- Alex Ekwueme Federal University, Ndufu-Alike (NG)
- Venue
- Energy Informatics
- Published
- 2024-02-07
- Volume
- 7
- Issue
- 1
- ISSN
- 2520-8942 (ISSN-L)
- OpenAlex
- W4391616189
- Cited by
- 35
- References
- 113
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 9
- FWCI
- 13.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Energy Informatics (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Keywords
- big data analytics, climate change modeling, climate data analysis, climate change impacts, health impacts of climate change, natural resource distribution, sustainability, Sustainable Development Goals, implementation framework
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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