An Empirical Analysis of Deep Learning Models for Temperature Prediction

Accurate temperature prediction is critical in diverse areas, such as agriculture, disaster management, and urban planning, where understanding climatic patterns is essential. This study explores the application of advanced deep-learning models for temperature forecasting, focusing on the model’s ability to establish complex relationships and temporal dependencies within climatic data. This study evaluates the performance of various deep-learning models for temperature prediction using environmental data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional GRU (BiGRU) models were developed and compared. The models were trained on meteorological data from Ranchi, India, spanning 2014-2024. Performance was assessed using Root Mean Square Error (RMSE), loss function analysis, and statistical significance testing. Results indicate that bidirectional architectures (BiLSTM and BiGRU) consistently outperformed unidirectional models. BiLSTM achieved the lowest RMSE and most balanced loss values across training, validation and test sets. The BiLSTM model performed well by 39.19.7% in RMSE and 15.36% in test loss. From the statistical analysis, BiLSTM is the best performer compared with BiGRU, with a negative t-statistic (-29.65) and a very low p-value (0.00000771), indicating a statistically significant difference.