Deep Learning-Based Earthquake Prediction Technique Using Seismic Data.

Earthquakes are natural disasters that can cause severe damage to human life and infrastructure. Therefore, accurate earthquake prediction is crucial for disaster prepared-ness and risk reduction. Recently, machine learning techniques have shown promise in earthquake prediction. In this paper, we present a comprehensive study on the application of machine learning techniques for earthquake prediction. We first review the existing literature on earthquake prediction using machine learning techniques, including neural networks. We then propose a machine learning approach for earthquake prediction, based on analyzing seismic data. The proposed approach uses a convolutional neural network to extract relevant features from the seismic data, and a long short-term memory network to predict the probability of an earthquake. We evaluate the performance of the proposed approach on earthquake datasets from different regions and demonstrate its high accuracy in earthquake prediction. Our study provides a new perspective on earthquake prediction using machine learning techniques and highlights the potential of deep learning approaches for improving earthquake prediction. The proposed approach can be used in conjunction with existing earthquake prediction methods to provide more accurate and reliable predictions, which can help mitigate the potential impact of earthquakes on human life and infrastructure.