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@article{171108,
        author = {JUSTIN PAUL and JWALA JOSE and GIBI K S},
        title = {MACHINE LEARNING IN CLIMATE MODELING AND PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {4120-4124},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171108},
        abstract = {Climate modeling and prediction are critical for understanding the impacts of climate change and formulating effective mitigation and adaptation strategies. Traditional climate models, though highly detailed and robust, face limitations in computational complexity, long simulation times, and the need for vast amounts of data. Machine learning (ML) techniques, with their ability to uncover complex patterns from large datasets have emerged as a promising tool to enhance climate models. This paper reviews the application of machine learning in climate modeling and prediction, focusing on the integration of ML algorithms with traditional climate models, data-driven modeling approaches, and the challenges and future directions in the field [1].},
        keywords = {Climate Modeling, Machine Learning, Climate Prediction, Hybrid Models, Deep Learning, Climate Extremes, Explainable AI.},
        month = {January},
        }