Resumen
This paper proposes a novel method to incorporate unfavorable orientations of discontinuities into machine learning (ML) landslide prediction by using GIS-based kinematic analysis. Discontinuities, detected from photogrammetric and aerial LiDAR surveys, were included in the assessment of potential rock slope instability through GIS-based kinematic analysis. Results from the kinematic analysis, coupled with several commonly used landslide influencing factors, were adopted as input variables in ML models to predict landslides. In this paper, various ML models, such as random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and deep learning neural network (DLNN) models were evaluated. Results of two validation methods (confusion matrix and ROC curve) show that the involvement of discontinuity-related variables significantly improved the landslide predictive capability of these four models. Their addition demonstrated a minimum of 6% and 4% increase in the overall prediction accuracy and the area under curve (AUC), respectively. In addition, frequency ratio (FR) analysis showed good consistency between landslide probability that was characterized by FR values and discontinuity-related variables, indicating a high correlation. Both results of model validation and FR analysis highlight that inclusion of discontinuities into ML models can improve landslide prediction accuracy.