Hybrid deep learning for climate prediction with temporal, spatial, and environmental data
摘要
Climate change poses significant challenges to ecosystems, societies, and economies, demanding innovative solutions for accurate prediction and analysis. This study presents a novel hybrid deep learning framework integrating Temporal Convolutional Networks (TCNs), Convolutional Neural Networks (CNNs), and dense neural networks to address the complexities of climate prediction. By leveraging TCNs for temporal dependencies, CNNs for spatial pattern recognition, and dense networks for environmental and socioeconomic data analysis, the framework offers a unified approach to modeling diverse climate dynamics. Tested on multiple datasets, the model achieves superior accuracy and robustness, demonstrating its potential to generalize across various data modalities. Beyond improving prediction precision, the framework provides actionable insights into climate drivers such as CO