Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time

被引：0
|
作者：
Chen M. [1 ,2 ]
Tao P. [1 ]
Zhou C. [2 ]
Jiang Q. [2 ]
机构：
[1] School of Civil Engineering and Architecture, Guangxi University, Nanning
[2] School of Civil Engineering, Wuhan University, Wuhan
来源：
Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences | 2024年 / 49卷 / 05期
关键词：
forecasting decision; hazards; landslide time-of-failure forecast; landslides; model modification; probabilistic analysis; Verhulst inverse-function model;
D O I：
10.3799/dqkx.2023.003
中图分类号：
学科分类号：
摘要：
Landslide time-of-failure forecast is an essential part of landslide disaster prevention and control. However, due to the uncertainty of the landslide evolution process, it is challenging to forecast the occurrence time of landslide events accurately. The Verhulst inverse-function model is a common landslide time-of-failure forecasting model, but the model suffers from the problems of poor fitting quality and low forecasting accuracy of displacement monitoring data caused by the improper selection of the calculation starting points. To address this deficiency, an improved Verhulst inverse-function model (MVIF model) is proposed and analyzed for near real-time probabilistic forecast.The results show that (1) the MVIF model addresses the problem of harsh selection of the calculation starting points in the original model; (2) the MVIF model has high forecasting accuracy and can make reliable forecasts after the landslide enters the medium accelerating deformation phase; (3) the combination of predicted landslide time and failure probability provides a new forecasting criterion. This study can provide valuable reference for early warning and forecast of creeping landslides. © 2024 China University of Geosciences. All rights reserved.
引用
收藏
页码：1692 / 1705
页数：13
相关论文
共 40 条
  • [1] Amitrano D., Grasso J. R., Senfaute G., Seismic Precursory Patterns before a Cliff Collapse and Critical Point Phenomena, Geophysical Research Letters, 32, 8, (2005)
  • [2] Carey J. M., Moore R., Petley D., Et al., Pre - Failure Behaviour of Slope Materials and Their Significance in the Progressive Failure of Landslides, Landslides and Climate Change: Challenges and Solutions, pp. 207-215, (2007)
  • [3] Carla T., Farina P., Intrieri E., Et al., On the Monitoring and Early-Warning of Brittle Slope Failures in Hard Rock Masses: Examples from an Open - Pit Mine, Engineering Geology, 228, pp. 71-81, (2017)
  • [4] Carla T., Intrieri E., Di Traglia F., Et al., Guidelines on the Use of Inverse Velocity Method as a Tool for Setting Alarm Thresholds and Forecasting Landslides and Structure Collapses, Landslides, 14, 2, pp. 517-534, (2017)
  • [5] Carla T., Farina P., Intrieri E., Et al., Integration of Ground-Based Radar and Satellite InSAR Data for the Analysis of an Unexpected Slope Failure in an Open-Pit Mine, Engineering Geology, 235, pp. 39-52, (2018)
  • [6] Chen H., Tang H., Ge X.R., Et al., Study on Early Warning Index and Early Warning Forecast of Creep Landslide Based on Deep Displacement, Chinese Journal of Rock Mechanics and Engineering, 38, pp. 3015-3024, (2019)
  • [7] Crosta G. B., Agliardi F., Failure Forecast for Large Rock Slides by Surface Displacement Measurements, Canadian Geotechnical Journal, 40, 1, pp. 176-191, (2003)
  • [8] Fang S.A., Xu Q., Xiu D.H., Et al., A Study of the Predicted Instability Time of Sudden Loess Landslides Based on the SLO Model, Hydrogeology & Engineering Geology, 48, 4, pp. 169-179, (2021)
  • [9] Fukuzono T., A New Method for Predicting the Failure Time of a Slope, The Fourth International Conference and Field Workshop on Landslides, pp. 145-150, (1985)
  • [10] Gigli G., Fanti R., Canuti P., Et al., Integration of Advanced Monitoring and Numerical Modeling Techniques for the Complete Risk Scenario Analysis of Rock-slides: The Case of Mt. Beni (Florence, Italy), Engineering Geology, 120, pp. 48-59, (2011)
1234