Machine Learning for Predicting Climate Change: A Data-Driven Approach
Year: 2025
Type: article
Abstract: With its profound effects on ecosystems, human communities, and economies, climate change has emerged as one of the 21st century's most urgent issues. Accurate climate change prediction is essential for putting mitigation and adaptation plans into action on time. The computing needs and uncertainties involved in forecasting long-term climate changes frequently provide a challenge for traditional climate modeling approaches, which rely on data from intricate environmental systems and physical simulations. However, a different strategy is offered by recent developments in machine learning (ML) techniques, which use enormous volumes of environmental data to build models that can produce predictions with greater accuracy. By using a variety of data-driven approaches on environmental datasets, this study investigates the application of machine learning techniques for climate change prediction. By using this method, the study hopes to add to the expanding corpus of research on how machine learning might enhance climate forecasting by enhancing conventional models.
Author: Asst. Prof. Akash Uday Shirke
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W6911692245
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