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Applicability of artificial intelligence to reservoir induced earthquakes

Samui P., Kim D.

Acta Geophysica

2014

Vol. 62, no. 3

608--619

This paper proposes to use least square support vector machine (LSSVM) and relevance vector machine (RVM) for prediction of the magnitude (M) of induced earthquakes based on reservoir parameters. Comprehensive parameter (E) and maximum reservoir depth (H) are used as input variables of the LSSVM and RVM. The output of the LSSVM and RVM is M. Equations have been presented based on the developed LSSVM and RVM. The developed RVM also gives variance of the predicted M. A comparative study has been carried out between the developed LSSVM, RVM, artificial neural network (ANN), and linear regression models. Finally, the results demonstrate the effectiveness and efficiency of the LSSVM and RVM models.

A study of slope stability prediction using least square support vector machine

Samui P.

International Journal of Applied Mechanics and Engineering

2012

Vol. 17, no. 1

279-287

The determination of stability of slope is an important task in geological engineering practice. This paper proposes the use of the least square support vector machine (LSSVM) for the determination of stability of slope. The LSSVM is a statistical learning method which has a self-contained basis of statistical-learning theory and excellent learning performance. The five input variables used for the LSSVM model in this study are the unit weight (d), cohesion (c), angle of internal friction, slope angle, height (H) and pore water pressure coefficient (ru). The LSVM model also gives a probabilistic output. This study shows that the LSSVM model is a robust tool for slope stability analysis.