防灾与环境

库岸堆积层滑坡位移区间混合预测方法研究

  • 邓自强 ,
  • 李麟玮 ,
  • 向喜琼 ,
  • 吴益平 ,
  • 苗发盛
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  • 1.贵州大学 资源与环境工程学院,贵阳 550025;
    2.贵州大学 喀斯特地质资源与环境教育部重点实验室,贵阳 550025;
    3.中国地质大学 工程学院,武汉 430074
邓自强(1998—),男,四川自贡人,硕士生,主要从事滑坡灾害预测预报方面的研究工作。E-mail:dzq18227752501@126.com
李麟玮 (1993—),男,贵阳人,博士,讲师,主要从事地质工程领域的教学与科研工作。E-mail:lwli@gzu.edu.com

收稿日期: 2024-05-11

  网络出版日期: 2025-03-12

基金资助

贵州省基础研究(自然科学)项目(黔科合基础-ZK〔2023〕一般066);贵州省科技支撑项目(黔科合支撑〔2023〕一般127);国家自然科学基金(42367022);国家重点研发计划(2022YFC300330)

Research on Hybrid Prediction Method for Displacement Intervals of Reservoir Colluvial Landslides

  • Deng Ziqiang ,
  • Li Linwei ,
  • Xiang Xiqiong ,
  • Wu Yiping ,
  • Miao Fasheng
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  • 1. College of Resources and Environmental Engineering, Guizhou University, Guiyang 500025,P.R. China;
    2. China Key Laboratory of Karst Georesources and Environment Ministry of Education, Guizhou University, Guiyang 500025, P.R. China;
    3. Faculty of Engineering, China University of Geosciences, Wuhan 430074, P.R. China

Received date: 2024-05-11

  Online published: 2025-03-12

摘要

为解决传统方法中存在的位移分量物理意义不准确和预测区间计算效率不高两大难点问题,提出一种新型库岸堆积层滑坡位移区间混合预测方法。首先,采用临界水平模型建立趋势性位移与库水位变动特征参数之间函数关系,实现趋势性位移成分的预测,并结合时序分解模型,获取周期性位移成分;然后,采用上下限估计框架下的多目标灰狼优化双体式支持向量回归机对周期性位移区间进行预测;最后,对趋势性位移与周期性位移的预测结果进行组合,构建累积位移预测区间。通过白水河滑坡实例验证,所提出方法在具备更高计算效率的同时,预测精度与预测区间质量较高,在滑坡灾害预测预报领域具有较高的实用价值。

本文引用格式

邓自强 , 李麟玮 , 向喜琼 , 吴益平 , 苗发盛 . 库岸堆积层滑坡位移区间混合预测方法研究[J]. 地下空间与工程学报, 2025 , 21(1) : 339 -349 . DOI: 10.20174/j.JUSE.2025.01.37

Abstract

A novel hybrid interval prediction method of reservoir colluvial landslides with step-like displacements is proposed to solve two significant difficulties in traditional methods, i.e., the inaccurate physical meaning of displacement components and the low computational efficiency of interval prediction models. According to the mechanism of landslides, the critical level model was adopted to establish the mathematical function to achieve the prediction of trend displacement components at first. And then, based on the trend components, the periodic displacement components were obtained by applying the time series decomposition model. After that, the Multi-Objective Grey Wolf Optimization-based Support Vector Regression model under the Upper and Lower Bound Estimation was adopted to construct the prediction interval of periodic displacements. Finally, the prediction results of trend displacement and periodic displacement components were combined to build the cumulative displacement prediction interval. Through the verification of the Baishuihe landslide case, the hybrid model has high computational efficiency and accuracy with high-quality prediction intervals.

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