Research on Regional Temperature Anomaly Detection and Climate Change Trend Prediction Based on Deep Learning

Global climate change is one of the most pressing environmental challenges of the 21st century. Traditional climate prediction methods have significant limitations in capturing complex temperature variation patterns, necessitating the development of innovative approaches. This study proposes a multimodal temperature anomaly detection and climate trend prediction method based on deep learning. By integrating linear regression with a long short-term memory (LSTM) network, the approach enhances the accuracy and reliability of global temperature predictions. The study employs the Berkeley Earth Surface Temperature dataset, which contains global historical temperature data from 1750 to 2015. The proposed multimodal deep learning model consists of two branches: linear regression, which captures long-term trends, and LSTM, which models nonlinear temperature variations. A systematic data preprocessing pipeline was developed, incorporating missing value handling, time series normalization, and advanced feature engineering, significantly improving data utilization efficiency. Experimental results demonstrate that the proposed method outperforms traditional statistical approaches and existing deep learning models across multiple evaluation metrics. Compared with the Transformer model, it achieves reductions of 7.1% in mean square error (MSE), 3.6% in root mean square error (RMSE), and 4.2% in mean absolute error (MAE). This research not only introduces an innovative framework to academia but also establishes a new technical paradigm for climate change studies, contributing to climate response strategy formulation.