Development of predictive charts for landslide runout based on Monte Carlo simulation and support vector regression

Horizontal runout distance prediction of potential landslides is of great significance in hazard mitigation.In this study, predictive charts of landslide horizontal runout distance were developed based on the support vector regression (SVR) algorithm and Monte Carlo (MC) modeling. An SVR-based prediction model was constructed using a dataset of 424 historical landslides, which included six parameters: triggers, mass materials, volume, slope gradient, vertical drop, and horizontal runout distance. The first five parameters were employed as predictive indicators to estimate horizontal runout distance. To investigate the optimization of the penalty factor (c) and influence parameter in the kernel function (g) and their effects on prediction accuracy, 21 conditions were tested with 7 training/testing ratios (7/1, 6/2, 5/3, 4/4, 3/5, 2/6, and 1/7) in combination with three kernel functions: linear, radial basis function (RBF), and sigmoid. Predictive charts were then created by adopting the MC method to account for uncertainties in slope volume and slope gradient parameters. The results show that (1) the coefficient of determination in each condition was greater than 0.825, with the highest value of 0.854 obtained under the condition of a 7/1 training/testing ratio in combination with the RBF kernel function; (2) increasing the training/testing ratio improved prediction accuracy; (3) the model with the RBF kernel function performed better than those with other kernel functions; and (4) the optimization of c and g parameters significantly improved prediction accuracy. The feasibility and efficiency of the proposed model were demonstrated using a practical case of the Zhonghai Village landslide, highlighting the potential of SVR combined with MC modeling for landslide runout prediction and hazard mitigation.