Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification

In this study an earthquake predictor system is proposed by combining seismic indicators along with Genetic Programming (GP) and AdaBoost (GP-AdaBoost) based ensemble method. Seismic indicators are computed through a novel methodology in which, the indicators are computed to obtain maximum information regarding seismic state of the region. The computed seismic indicators are used with GP-AdaBoost algorithm to develop an Earthquake Predictor system (EP-GPBoost). The setup has been arranged to provide predictions for earthquakes of magnitude 5.0 and above, fifteen days prior to the earthquake. The regions of Hindukush, Chile and Southern California are considered for experimentation. The EP-GPBoost has produced noticeable improvement in earthquake prediction due to collaboration of strong searching and boosting capabilities of GP and AdaBoost, respectively. The earthquake predictor system shows enhanced results in terms of accuracy, precision and Matthews Correlation Coefficient for the three considered regions in comparison to contemporary results.