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 Random Tree classifier achieved superior predictive performance on the slope stability dataset, attaining an accuracy of 95.33% across both training and test phases, which surpasses the J48 model (86.92%) and other recently proposed models, as evidenced by comparative metrics including MCC, precision, recall, and F-measure.