Research on earthquake prediction based on BP and SOM neural network

Abstract:

Because of the complexity of the causes of earthquake prediction，it has been recognized an aporia by all over the world. In this paper, a new method based on Back-Propagation neural network (BP) and Self-Organizing Feature Map neural network (SOM) is proposed，and applied to the prediction of earthquake magnitude. Firstly, Clustering of the original seismic data by using Self-Organizing Feature Map neural network,, which has the inherent law of the samples together, after using BP neural network to the sample data for learning and prediction, the experimental results show that compared with linear regression prediction model and BP neural network prediction results, the increase of SOM clustering process can effectively reduce the prediction error. It shows that this method can effectively summarize the factors which are closely related to earthquakes and SOM is effective for the classification of the relevant magnitude parameters, and it can be as an effective assistant method to predict the magnitude by using the fuzzy prediction method.