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Open Access
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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n
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Anvesha Katti, Priyanka Vashisht,
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- Assistant Professor, Associate Professor, Department of Computer Science and Engineering, Amity University, Department of Computer Science and Engineering, Amity University, Haryana, Haryana, India, India
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Abstract
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nClimate modeling helps to predict the future of climate variations and human interference with environment. The traditional General Circulation Models (GCMs) are based on physics-derived mathematical equations but are very expensive in terms of computation. There are alternative ways to perform climate modeling in recent years with the rise and improvement of Artificial Intelligence (AI) based approaches in term of predictability, efficiency, and classification of extreme events compared to conventional. In this study, AI Models like XGBoost, Long Short-Term Memory (LSTM) networks and hybrid AI-physics models are assessed over GCM. Results show that AI models significantly decrease prediction errors by about 30%, with hybrid AI-physics models having the lower MAE and higher F1 score for extreme event classification. In addition, the AI models have improved computational power by 40% less time for processing and less energy consumption. These results highlight the possibility that AI based approaches can be highly effective for enhancing climate prediction accuracy, risk assessment, and informing policies.nn
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Keywords: Climate modeling, artificial intelligence, machine learning, general circulation models, extreme event classification, hybrid ai-physics models, computational efficiency, climate prediction
n[if 424 equals=”Regular Issue”][This article belongs to Current Trends in Information Technology ]
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nAnvesha Katti, Priyanka Vashisht. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Comparative Analysis of AI-Based Approach vs. Traditional Methods in Climate Modeling[/if 2584]. Current Trends in Information Technology. 17/09/2025; 15(03):26-32.
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nAnvesha Katti, Priyanka Vashisht. [if 2584 equals=”][226 striphtml=1][else]Comparative Analysis of AI-Based Approach vs. Traditional Methods in Climate Modeling[/if 2584]. Current Trends in Information Technology. 17/09/2025; 15(03):26-32. Available from: https://journals.stmjournals.com/ctit/article=17/09/2025/view=0
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| Volume | 15 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 03 | |
| Received | 12/05/2025 | |
| Accepted | 28/08/2025 | |
| Published | 17/09/2025 | |
| Retracted | ||
| Publication Time | 128 Days |
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