Earthquake Early Warning System Utilizing an CNN-LSTM-TL Based Method for Detection and Parameters Classification

The obvious first line of protection against powerful earthquake motion is to reinforce houses and other structures. The goal of real-time earthquake catastrophe prevention, in contrast to real-time seismology, is to mitigate damage while an earthquake is still underway. A disaster preventive measure that can be put into action in real-time in the event of an earthquake requires an early warning system (EEW). In order to avoid disasters, should not rely solely on EEW. The order of preprocessing, feature selection, and training the model must be meticulously followed. The preparation phase includes data encoding and normalization. Feature selection incorporates principal component analysis and linear discriminant analysis. It utilized CNN-LSTM-TL for the model's training. The results demonstrate a remarkable 96.49% accuracy.