Predicting Slope Stability Using Tree-Based Classifiers: Comparative Study of J48 and Random Tree Methods

The assessment of slope stability constitutes an essential to geotechnical analysis and geological research due to the catastrophic consequences associated with slope failures in many countries.This research focuses upon the development of new decision tree models, namely Random Tree and J48, for predicting slope stability.The models were constructed and validated using a dataset consisting about 107 slope stability case histories.This dataset includes six key parameters: unit weight, cohesion, angle of internal friction, slope angle, slope height, and pore water ratio, all of which are essential for assessing slope stability.The efficient functioning of the recommended models is assessed using performance measures, including the accuracy (ACC), Matthews's correlation coefficient (MCC), precision, recall, and F-measure.The analysis of the ACC together with MCC, precision, recall, and F-measure for the slope stability data set shows that the random tree model achieved a better prediction performance compared to the J48 and the recently developed models in literature.