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@article{175251,
        author = {Ravi Ranjan},
        title = {Evaluating Deep Learning Models for Predicting Climate Change Trends: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2326-2329},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175251},
        abstract = {Climate change prediction is critical for understanding and mitigating the impacts of global warming. Traditional climate models, though robust, often struggle with the complexity and vastness of climate data. In recent years, deep learning has emerged as a powerful tool for analyzing and predicting climate trends due to its ability to handle large datasets and capture intricate patterns. This review evaluates various deep learning models used for predicting climate change trends, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Generative Adversarial Networks (GANs). We assess their performance, data requirements, and suitability for different climate variables such as temperature, precipitation, and extreme weather events. Furthermore, we discuss the challenges and limitations associated with deep learning models in climate science, such as data quality, interpretability, and computational demands. This review aims to provide insights into the current state of deep learning in climate change prediction and highlight future research directions to enhance model accuracy and reliability.},
        keywords = {climate change, prediction, machine learning, neural network, temperature data},
        month = {April},
        }