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An end-to-end deep learning model, DLPGA, has been proposed for predicting peak ground acceleration (PGA) in earthquake early warning systems. The model uses a multilayer convolutional neural network to automatically extract features from seismic wave data, resulting in improved accuracy and timeliness compared to traditional methods. The model has been tested and shown to have better generalization ability, making it a valuable tool for predicting the destructiveness of ground motion in real-time.
