降雨-融雪作用下滑坡变形机制分析及位移预测-以意大利圣安德烈滑坡为例

doi:10.3799/dqkx.2026.140

降雨-融雪作用下滑坡变形机制分析及位移预测-以意大利圣安德烈滑坡为例

doi: 10.3799/dqkx.2026.140

1. 北华大学土木与交通学院, 吉林吉林 132013;
2. 防灾减灾湖北省重点实验室(三峡大学）, 湖北宜昌 443002;
3. 帕多瓦大学土木、环境与建筑工程学院, 意大利帕多瓦 35131

基金项目:

吉林省教育厅科学研究项目(No.JJKH20261500K)

防灾减灾湖北省重点实验室(三峡大学)开放基金(No.2025KJZ04)

中国地震局工程力学研究所基本科研业务费专项资助项目(No.2026D02)。

详细信息

作者简介:
刘昱廷(1995-),男,讲师,主要从事地质灾害机理与防治方面的研究。ORCID:0000-0001-8559-2364.E-mail:yuting.liu@beihua.edu.cn

通讯作者:
庞辉,副教授,主要从事工程防灾、减震隔震方面的研究。ORCID:0009-0007-8919-0451.E-mail:panghui0615@163.com

中图分类号: P642.22
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出版历程
- 收稿日期: 2026-02-28
- 网络出版日期: 2026-06-29

Deformation Mechanism Analysis and Displacement Prediction of the Sant’Andrea Landslide influenced by Rainfall and Snowmelt, Italy

1. School of Civil Engineering and Transportation, Beihua University, Jilin, 132013, China;
2. Hubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University), Yichang 443002, Hubei, China;
3. Department of Civil, Environmental and Architectural Engineering, University of Padua, Via Ognissanti, 39, 35131 Padua, Italy

摘要

摘要: 在高海拔山区，滑坡稳定性同时受降雨与降雪融雪共同影响。系统地量化降雨和降雪累积与融化过程对滑坡稳定性影响和灾害评估具有重要意义。本研究提出一种基于图像监测的积雪自动识别与量化方法，通过图像处理识别计算图像中的积雪体素指标，并结合降雨、气温、湿度及历史位移等多源时序数据，以深度学习神经网络为基础构建滑坡位移预测模型，并以意大利圣安德烈滑坡为研究对象进行验证。在考虑积雪因素条件下，模型能够有效预测滑坡地表位移变化，结果表明滑坡位移对融雪过程具有显著响应特征，降雪及融雪过程显著影响滑坡变形速率。通过引入基于图像的积雪识别，改进了仅以降雨为输入的位移预测模型，显著提高了预测准确度，为寒区滑坡监测预报提供了新的技术途径。
- 滑坡监测 /
- 降雪识别 /
- 深度学习 /
- 位移预测 /
- 寒区边坡
Abstract: In high-altitude mountainous regions, landslide stability is jointly influenced by rainfall and snow fall-melting processes. Systematically quantifying the effects of precipitation and snow dynamics on landslide stability is essential for hazard assessment and risk mitigation. This study proposes an automated snow recognition and quantification method based on photogrammetric monitoring. A snow voxel index is extracted from RGB images using image processing algorithms and integrated with multi-source time-series data, including rainfall, temperature, humidity, and historical surface displacement. A deep learning neural network-based model is then developed to predict landslide displacement, and its performance is validated using the Sant’Andrea landslide in Italy as a case study. By incorporating snow-related factors, the model effectively predicts surface displacement variations. The results demonstrate that landslide displacement exhibits a significant response to snowmelt processes, indicating that snowfall and subsequent melting play an important role in accelerating landslide displacement rate. Compared with conventional models using rainfall-only inputs, the inclusion of image-derived snow voxel indicators significantly improves prediction accuracy. This study demonstrates that integrating image-based snow monitoring with deep learning provides an effective technical framework for landslide monitoring and prediction in cold regions.
- landslide monitoring /
- snow recognition /
- deep learning /
- displacement prediction /
- cold-region landslides

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降雨-融雪作用下滑坡变形机制分析及位移预测-以意大利圣安德烈滑坡为例

doi: 10.3799/dqkx.2026.140

作者简介:
刘昱廷(1995-),男,讲师,主要从事地质灾害机理与防治方面的研究。ORCID:0000-0001-8559-2364.E-mail:yuting.liu@beihua.edu.cn

通讯作者:
庞辉,副教授,主要从事工程防灾、减震隔震方面的研究。ORCID:0009-0007-8919-0451.E-mail:panghui0615@163.com

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Deformation Mechanism Analysis and Displacement Prediction of the Sant’Andrea Landslide influenced by Rainfall and Snowmelt, Italy

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留言板

降雨-融雪作用下滑坡变形机制分析及位移预测-以意大利圣安德烈滑坡为例

doi: 10.3799/dqkx.2026.140

作者简介: 刘昱廷(1995-),男,讲师,主要从事地质灾害机理与防治方面的研究。ORCID:0000-0001-8559-2364.E-mail:yuting.liu@beihua.edu.cn

通讯作者: 庞辉,副教授,主要从事工程防灾、减震隔震方面的研究。ORCID:0009-0007-8919-0451.E-mail:panghui0615@163.com

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出版历程

Deformation Mechanism Analysis and Displacement Prediction of the Sant’Andrea Landslide influenced by Rainfall and Snowmelt, Italy

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出版历程

目录

作者简介:
刘昱廷(1995-),男,讲师,主要从事地质灾害机理与防治方面的研究。ORCID:0000-0001-8559-2364.E-mail:yuting.liu@beihua.edu.cn

通讯作者:
庞辉,副教授,主要从事工程防灾、减震隔震方面的研究。ORCID:0009-0007-8919-0451.E-mail:panghui0615@163.com