Prediction Model of the Slope Angle of Rocky Slope Stability Based on Random Forest Algorithm

The slope angle of a slope is one of the important parameters affecting the stability of rocky slopes. In this paper, a new method based on the random forest (RF) algorithm is proposed to study the slope angle of rocky slopes. Based on the international typical rocky slope actual measurement data, the RF model for predicting the foot of the rocky slope is constructed by determining ten influencing factors affecting the slope angle of the rocky slope, namely, rock strength, rock quality designation (RQD), joint spacing, continuity, openness, roughness, filling, weathering, groundwater, and engineering direction as independent variables. The experimental results show that (1) the RF model has the smallest out-of-bag error when the number of decision trees n(tree) is four and the number of features in the split feature set m(try) is five hundred; (2) engineering direction, fill degree, RQD, groundwater, and joint spacing have a large influence on the foot of a rocky slope; (3) relative to artificial neural networks (BP), artificial neural networks optimized by genetic algorithm (GA-BP), support vector machine (SVM), and multiple linear regression (MLR), the RF regression model has obvious advantages in terms of prediction accuracy and model stability, which provides an effective method for achieving accurate prediction of slope angle of rocky slopes.