Attention-Enhanced Temporal Deep Learning with Explainable AI for Dew Point Temperature Forecasting
Abstract
Accurate dew point temperature predictions are vital for weather forecasting, agriculture, energy management and environmental monitoring, especially in climate sensitive areas. The traditional statistical method and shallow learning method have some difficulties in the modeling of complicated nonlinear temporal correlation relationship of multi-variate meteorological information. In this paper, this work introduces an attention-based deep learning approach to forecast dew point temperature in the Kaggle Weather Dataset. The proposed approach involves feature engineering, application of SHAP scores for feature selection, Min-Max normalization, and temporal data processing, to increase the predictive accuracy of the model and its interpretability. The model thoroughly evaluates multiple deep learning architectures (LSTM, CNN, CNN-LSTM, attention based models, BiLSTM, GRU, TCN, and hybrid models) based on RMSE, MAE, R2, Adjusted R2, test loss and validation loss. The experimental results confirm that the suggested BiLSTM–BiGRU hybrid model has the best predictive performance with an RMSE of 0.0163 and a R2 score of 0.9938 compared to the other examined models. SHAP, LIME, and ELI5 enhance the transparency of the models, such as global and local descriptions of feature contributions. Additionally, the trained model is integrated into a Flask-based web application that predicts the dew point in real-time and visualizes the results, demonstrating its practical application for decision support systems in climate conditions.