Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data

Abstract

Indonesia resides on most earthquake region with more than 100 active volcanoes, and high number of seismic activities per year. In order to reduce the casualty, some method to predict earthquake have been developed to estimate the seismic movement. However, most prediction use only short term of historical data to predict the incoming earthquake, which has limitation on model performance. This work uses medium to long term earthquake historical data that were collected from 2 local government bodies and 8 legitimate international sources. We make an estimation of a mediumto-long term prediction via machine learning algorithms, which are multinomial logistic regression, support vector machine and Näive Bayes, and compares their performance. This work shows that the support vector machine outperforms other method. We compare the root mean square error computation results that lead us into how concentrated data is around the line of best fit, where the multinomial logistic regression is 0.777, Näive Bayes is 0.922 and support vector machine is 0.751. In predicting future earthquake, support vector machine outperforms other two methods that produce significant distance and magnitude to current earthquake report.