Development and Evaluation of Machine Learning Models for Analysing Computational Climate Data

DOI

10.2991/978-94-6463-948-3_30How to use a DOI?

Keywords

Computational Modelling; Predictive Modelling; Linear Regression; Random Forest; Gradient Boosting; Logistic Regression

Abstract

Climate change, caused primarily by man-made activities like greenhouse gas emission and deforestation, causes global warming, weather anomalies and rise in ocean levels, threatening the ecosystem and the lives of man critically. To resolve these issues, our project suggests a machine learning approach to climatic information analysis, which supposes reliance on computational modelling. Five models are developed with the use of the Linear Regression, Random Forest Regression, Gradient Boosting, Random Forest Classifier, and Logistic Regression to predict main variables such as temperature and CO2 levels and predict the development of the environmental risks. They were compared and proven on an accuracy measure such as R 2 Score, MAE and RMSE and Gradient Boosting had greater predictive power. The effectiveness and accuracy of our solution are superior to the traditional methods by the ability of machine learning to process complex and non-linear data sets. Through the models, the policymakers, researchers, and planners can be in a position to know the climatic patterns, anticipate the future occurrences and give evidence-based recommendations to prevent the adverse effects of climatic change.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jupinder Kaur
AU  - Atharav Kagde
AU  - Mayur Purohit
AU  - Jay Soni
PY  - 2026
DA  - 2026/01/06
TI  - Development and Evaluation of Machine Learning Models for Analysing Computational Climate Data
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 419
EP  - 439
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_30
DO  - 10.2991/978-94-6463-948-3_30
ID  - Kaur2026
ER  -