EARTHQUAKE PREDICTION USING ATTENTION MECHANISM IN DEEP LEARNING

Abstract

Earthquakes are one of the natural phenomena which have incessantly caused break and loss of human life in olden times. Earthquake prediction is an important aspect of any society's plans and can boost public preparedness and decrease damage to a great extent. Due to the stochastic character of earthquakes and the challenge of achieving an efficient and dependable model for earthquake prediction, efforts have been insufficient thus far, and new methods are required to solve this problem. This paper proposes a novel prediction method based on attention mechanism using Deep learning which can predict the number and maximum magnitude of earthquakes in each area of the region. This model focuses on effective earthquake characteristics and produces more accurate predictions. Firstly, pre-processing on earthquake data set is applied. Secondly, to effectively use spatial information and reduce dimensions of input data, the deep learning algorithm is used to capture the spatial dependencies between earthquake data. Thirdly, RNN is employed to capture the temporal dependencies. Fourthly, the Attention Mechanism layer is introduced to highlight its important features to achieve better prediction performance. The results show that the proposed method has better performance and generalize ability than other prediction methods.