Machine learning applied to the prediction of rockfall slope probability.

Abstract

The objective of this work is to propose a predictive model of rockfall slope probability in rock slopes using the KNearest Neighbors (KNN) method. A dataset composed by 220 rock slopes was used, whose variables are related to the presence of water, characteristics of the rock mass, degree of overhang, among others. For each slope of the dataset, rockfall probability (high, medium, or low) is known and determined by cluster analysis. The number of the nearest neighbors (k) ranged from 1 to 20. The obtained average accuracy of the tested predictive models was equal to 78.4%. The models produced satisfactory results in the prediction of the rockfall probability, since the area under the ROC curve was equal to 0.80. The best model was selected based on the k value with the highest accuracy and the highest area under the ROC curve. The selected model had a k value equal to 7.