Table 1.
Comparative analysis of different approaches used for extreme heat prediction.
Fig 1.
Proposed methodology for predicting extreme heat events.
Fig 2.
Monthly average maximum temperature of study region over the past 5 years.
Table 2.
Details of XDL dataset parameters.
Fig 3.
Correlation Heatmap of XWeather dataset parameters.
Fig 4.
ANN structure for extreme heat prediction.
Fig 5.
CNN structure for extreme heat prediction.
Fig 6.
Detailed gate structure of LSTM architecture reveals intricate insights into long short-term memory networks.
Fig 7.
LSTM structure for extreme heat prediction.
Table 3.
Hyperparameter values for deep learning models.
Fig 8.
Training vs. validation loss of ANN model.
Fig 9.
Training VS. validation accuracy of ANN model.
Fig 10.
Training vs. validation loss of CNN model.
Fig 11.
Training vs. validation accuracy of CNN model.
Fig 12.
Training vs. validation loss of LSTM model.
Fig 13.
Training vs. validation accuracy of LSTM model.
Fig 14.
Performance comparison of deep learning models.
Table 4.
Comparison of models based on accuracy, MSE, MAE, and RMSE.
Fig 15.
Comparison of actual and predicted heat events: a deep learning perspective.
Table 5.
Predicted extreme heat events and actual temperatures in Lahore.
Fig 16.
Understanding the SHAP XAI process.
Table 6.
Results of different models on various datasets.
Fig 17.
Visualizing XAI SHAP model results for deep learning insight.
Fig 18.
Visual representation of XAI LIME model results.