Verhulst反函数预测模型的改进及滑坡时间概率预测
Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time
陈铭熙 1陶培捷 2周创兵 3姜清辉3
作者信息
- 1. 广西大学土木建筑工程学院,广西南宁 530004;武汉大学土木建筑工程学院,湖北武汉 430072
- 2. 广西大学土木建筑工程学院,广西南宁 530004
- 3. 武汉大学土木建筑工程学院,湖北武汉 430072
- 折叠
摘要
滑坡时间预测是滑坡灾害防治的重要组成部分,但由于滑坡演化存在不确定性,准确预测滑坡的发生极为困难.Verhulst反函数模型是一种常用的滑坡时间预测模型,但其存在计算起始时刻选择不当会造成监测数据拟合质量差和预测精度低的问题.针对这一不足,提出了一种改进的Verhulst反函数模型(MVIF模型),并进行近实时概率预测分析.结果表明:(1)MVIF模型改善了原模型对计算起始时刻选择苛刻的问题;(2)MVIF模型预测精度较高,在滑坡进入中等加速变形阶段之后可进行可靠的预测;(3)预测滑坡时间与破坏概率结合提供了一种新的滑坡预报准则.该研究可为蠕滑型滑坡的预警预报提供有价值的参考.
Abstract
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.
关键词
滑坡/滑坡时间预测/Verhulst反函数模型/模型改进/概率分析/预测决策/灾害Key words
landslides/landslide time-of-failure forecast/Verhulst inverse-function model/model modification/probabilistic analysis/forecasting decision/hazards引用本文复制引用
基金项目
广西科技基地和人才专项(AD23026111)
广西自然科学基金(2024GXNSFBA010226)
广西壮族自治区科技重大专项(AA23023016)
出版年
2024
NETL
NSTL
万方数据