Big Data and Environmental Prediction: Moving from Stations to Satellites (2015–2025)

Jingzhi Zhang

doi:10.54254/2755-2721/2026.TJ28915

Applied and Computational EngineeringOpen access

Big Data and Environmental Prediction: Moving from Stations to Satellites (2015–2025)

Research Article

Open Access

Big Data and Environmental Prediction: Moving from Stations to Satellites (2015–2025)

Jingzhi Zhang ^1*

¹ Dulwich International High School Suzhou, China, 215021

^*Corresponding author: ingrid.zhang26@stu.dulwich.org

Published on 5 November 2025

ACE Vol.203

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-515-8

ISBN (Online): 978-1-80590-516-5

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Abstract

Among the most significant issues societies are air pollution and environmental change. They impact natural ecosystems and agriculture in addition to human health. Previous studies frequently relied on data from a small number of nearby stations, which constrained the scope of the analysis. Recent investigations integrate expansive datasets derived from satellite observations, meteorological sources, and monitoring infrastructures. Furthermore, the application of big data analytics has been instrumental in diverse environmental domains, spanning ecosystem conservation, climatological research, and water quality assessment, with a specific emphasis on air quality forecasting and allied ecological inquiries documented between 2015 and 2025. Linear models, tree-based models, deep learning techniques, and more sophisticated statistical tools are the primary categories of methods. Applications include monitoring biodiversity, predicting air pollution, and assessing climate risk. Deep learning is helpful when spatial and temporal patterns are complex, statistical methods assist in displaying degrees of certainty or uncertainty, and tree models are frequently dependable baselines. A lack of interpretability and inadequate testing techniques are obstacles. Future research should aim to strengthen validation, provide a explanation of uncertainty, and link data-driven methodologies to accepted scientific principles.

Keywords:

Air quality, Big data, Climate change, Ecosystem, Prediction

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References

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Cite this article

Zhang,J. (2025). Big Data and Environmental Prediction: Moving from Stations to Satellites (2015–2025). Applied and Computational Engineering,203,17-21.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-515-8(Print) / 978-1-80590-516-5(Online)

Editor: Marwan Omar, Guozheng Rao

Conference website: https://www.confspml.org/tianjin.html

Conference date: 21 December 2025

Series: Applied and Computational Engineering

Volume number: Vol.203

ISSN: 2755-2721(Print) / 2755-273X(Online)

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