Use of Graphical Data to Predict Earthquakes through Neural Network Approach

Natural calamities pose a risk to lives and properties causing extensive devastation and loss. It is crucial to have the ability to anticipate the likelihood of these disasters occurring to better prepare for and mitigate their impact. This study introduces an approach that combines ResNet and attention-based CNN to predict earthquakes. This approach efficiently extracts features from graphical data related to previous disasters by harnessing the strengths of both ResNet and attention-based CNN. The attention mechanism enables the model to concentrate on specific aspects of the input data, while ResNet's residual connection architecture facilitates training deep models without facing gradient vanishing issues. Experimental results showcase the effectiveness of this approach in predicting disasters, achieving an accuracy rate surpassing 90% for earthquakes. These results suggest that this method holds promise for improving disaster preparedness and mitigation efforts.