Advanced Deep Learning Models for Long-Term Climate Change Trend Forecasting

Jagriti Chand; Pooja Jayant; Swati Shrivastava

Advanced Deep Learning Models for Long-Term Climate Change Trend Forecasting

Author(s)	Ms. Jagriti Chand, Pooja Jayant, Swati Shrivastava
Country	India
Abstract	Climate change poses a critical global challenge, necessitating accurate and reliable forecasting of long-term climate trends to support informed policy-making and environmental planning. Traditional statistical and physical climate models often struggle to capture the complex, nonlinear relationships present in large-scale climate data. To address these limitations, this study presents advanced deep learning models for long-term climate change trend forecasting. The proposed approach leverages state-of-the-art architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to effectively model both spatial and temporal patterns in historical climate datasets. Multiple climate variables such as temperature, precipitation, humidity, and atmospheric pressure are utilized to enhance prediction accuracy. Comprehensive experiments are conducted on publicly available climate datasets spanning several decades. Model performance is evaluated using standard forecasting metrics, including mean absolute error and root mean square error, and is compared against conventional machine learning and statistical baseline models. The experimental results demonstrate that the proposed deep learning framework significantly improves long-term forecasting accuracy and robustness. The findings highlight the potential of deep learning-driven climate models to support early warning systems, climate risk assessment, and sustainable environmental decision-making in the face of accelerating climate change.
Keywords	Federated Learning, Privacy Preservation, Differential Privacy, Secure Multiparty Computation, Homomorphic Encryption
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-14

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Advanced Deep Learning Models for Long-Term Climate Change Trend Forecasting

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