Akademik Veri Yönetim Sistemi
Electronics (Switzerland), cilt.14, sa.16, 2025 (SCI-Expanded, Scopus)
Variations in global surface temperatures serve as critical indicators of climate change, and making accurate predictions regarding these patterns is essential for designing effective mitigation strategies. This study utilized a time series prediction methodology, leveraging annual temperature anomaly records from 1880 to 2022 provided by NASA’s GISTEMP v4 dataset. Following an extensive preprocessing phase, multiple deep learning models, namely, LSTM, DNN, CNN, and Transformer, were trained and analyzed separately. The individual model outputs were subsequently combined using a weighted averaging strategy grounded in linear regression, forming a novel LSTM and Transformer-based hybrid forecasting model. Model performance was assessed through widely recognized metrics including MSE, MAE, RMSE, and R2. By integrating the distinct advantages of each model, the ensemble framework aimed to improve the overall predictive capability. The findings revealed that this hybrid design delivered more stable and accurate forecasts compared to any single model. The integration of diverse neural network structures proved effective in boosting predictive reliability and underscored the viability of deep learning-based hybrid modeling for climate trend forecasting.