ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 13 - 2025 | doi: 10.3389/feart.2025.1666554
This article is part of the Research TopicPrevention, Mitigation, and Relief of Compound and Chained Natural Hazards, volume IIIView all articles
Development of predictive charts of landslide runout based on Monte Carlo and Support Vector Regression
Provisionally accepted
- 1Henan College of Transportation, Zhengzhou, China
- 2Hunan University, Changsha, China
- 3Guizhou Quality and Safety Traffic Engineering Monitoring and Testing Center Co. Ltd, Guiyang, China
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Horizontal runout distance prediction of potential landslides is of great significance in hazard mitigation. Based on the Support Vector Regression (SVR) algorithm and Monte-Carlo (MC) modeling, this study develops the predictive charts of landslide horizontal runout distance. Firstly, an SVR-based prediction model was built based on a dataset of 424 historical landslides, in which 6 parameters, triggers, mass materials, volume, slope gradient, and vertical drop were used as predictive indicators to predictive horizontal runout distance. Then, to investigate the optimization of (penalty factor) c, (Influence parameters in the kernel function) g parameters and its effect on prediction accuracy, 21 conditions were tested with 7 training/testing ratios of 7/1, 6/2, 5/3, 4/4, 3/5, 2/6, and 1/7 in combination with three kernel functions, namely, linear, radial basis function (RBF), and sigmoid. Finally, the predictive charts were created by adopting the MC method to consider the uncertainties of slope volume and sl52ope gradient parameters. The results show that: (1) the determination coefficient of each condition was greater than 0.825, with the highest one 0.854 was obtained with the condition of training/testing ratio of 7/1 in combination with the RBF kernel function. (2) Increase of training/testing ratio could increase the prediction accuracy; (3) The model with RBF kernel function performed better than model with other kernel functions; (4) The optimization of c, g parameters could significantly improve the prediction accuracy; (5) The feasibility and efficiency of the proposed model was demonstrated via a practical case of Zhonghai Village landslide.
Keywords: Landslide runout distance, Support vector regression for landslide, monte carlo, Predictive charts, slope
Received: 15 Jul 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 Kang, Zhiwen, Zhou, Sha and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xu Zhiwen, Hunan University, Changsha, China
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