Machine Learning models for seismic signals parameters

In this work, advanced machine learning algorithms are used to develop predictive models for forecasting ground motion parameters. The machine learning algorithms used are extreme learning machines (ELM), support vector regression (SVR) and its three variations, decision trees and hybrid algorithm ANFIS (adaptive neuro fuzzy inference system). A novel neuro fuzzy algorithm, RANFIS (randomized ANFIS) is also proposed for forecasting ground motion parameters. This advanced learning machine integrates the explicit knowledge of the fuzzy systems with the learning capabilities of neural networks, as in the case of conventional adaptive neuro fuzzy inference system (ANFIS). In RANFIS, to accelerate the learning speed without compromising the generalization capability, the fuzzy layer parameters are not tuned. The ground motion parameters predicted are peak ground acceleration (PGA), peak ground velocity (PGV) and peak ground displacement (PGD). The model is developed using real earthquake records obtained from the database released by PEER (Pacific Earthquake Engineering Research Center).