Earthquake Forecasting in the Himalayas Artificial Neural Networks

Abstract

Earthquake is a natural phenomenon that causes huge loss in both life and property. Improvement of seismic hazard assessment requires integrated techniques such as geodetic, stochastic, and machine learning models. Forecasting of the time of the event, magnitude, and location of the epicenter of future events has been the major focus of several efforts in recent years. Many methods have been proposed to forecast the occurrence of earthquakes like statistical methods and other modeling approaches. Such methods are based on either the study of electric or magnetic signals or microseismicity patterns in which changes are experienced due to an upcoming event. In this study, our aim is to forecast earthquakes using neural networks, based on some seismicity indicators which capture the intrinsic information of the earthquake events. For this, an effective neural network architecture is created with different deep learning optimization algorithms and the results showed that the eight seismicity indicators have essentially captured most of the information of earthquake events. It is observed that neural networks are an effective tool for forecasting earthquakes as the neural networks well capture the nonlinearity and heterogeneity of inherent mechanisms with appropriate weights. The proposed network provides 90% accuracy and an F1-score of 0.89. It is hoped that this study shall provide useful information to the industry, academia, and government agencies to develop new standards of monitoring and mitigation measures of earthquake hazard.