Deep Learning Approaches for Real-Time Climate Change Prediction Models

Abstract

This study presents novel deep learning architectures for real-time climate change prediction. We propose a hybrid transformer-LSTM model that integrates satellite imagery, atmospheric sensor data, and historical climate records to generate accurate short-term and long-term climate forecasts. Our model achieves a 23% improvement in prediction accuracy compared to existing methods, with particular strength in extreme weather event forecasting. The findings have significant implications for disaster preparedness and environmental policy planning.