BIG DATA ANALYTICS FOR CLIMATE CHANGE MODELING: A MULTIDISCIPLINARY COMPUTATIONAL APPROACH

Authors

  • Dr. Rabia Shahbaz Department of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, Pakistan. Author

Keywords:

Big Data Analytics, Climate Modeling, Environmental Informatics, Predictive Simulation

Abstract

Climate change poses a significant threat to global ecosystems and socioeconomic systems. Traditional modeling techniques are often limited in capturing the complex, nonlinear interactions in climate systems. Big Data Analytics (BDA) presents a transformative framework for enhancing climate change modeling through high-volume data integration, real-time processing, and advanced predictive capabilities. This study explores a multidisciplinary computational approach combining environmental science, machine learning, and cloud computing to refine climate predictions. It further analyzes case studies from Pakistan’s vulnerable zones to demonstrate how BDA can improve climate resilience strategies. The findings advocate for a shift from siloed climate models to integrated analytics platforms for real-time and long-term climate forecasting.

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Published

2023-04-06

Issue

Vol. 3 No. 2 (2023): Multidisciplinary Research in Computing Information Systems

Section

Articles

License

All articles published in the Multidisciplinary Research in Computing Information Systems are licensed under an open-access model. Authors retain full copyright and grant the journal the right of first publication. The content can be freely accessed, distributed, and reused for non-commercial purposes, provided proper citation is given to the original work.