Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{181494,
        author = {Jagriti Chand},
        title = {A Review on Deep Learning Models for Predicting Climate Change Trends},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {5248-5252},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181494},
        abstract = {Accurate climate change prediction is essential for anticipating and mitigating the adverse effects of global warming. While traditional climate modeling approaches offer foundational insights, they often face limitations when dealing with the high dimensionality and complexity of climate data. In recent years, deep learning has gained prominence as a transformative approach for modeling climate dynamics, owing to its capacity to process large-scale datasets and uncover complex, non-linear patterns. This review explores the application of various deep learning architectures—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Generative Adversarial Networks (GANs)—in forecasting climate change trends. We analyze their effectiveness, data dependencies, and adaptability to different climate variables such as temperature, rainfall, and extreme weather phenomena. Additionally, the review highlights key challenges in deploying deep learning for climate modeling, including issues related to data quality, interpretability, and computational scalability. The objective is to synthesize current progress in this field and outline promising research directions to enhance the precision, robustness, and practical applicability of deep learning models in climate science.},
        keywords = {climate change, prediction, machine learning, neural network, temperature data},
        month = {June},
        }