机理引导下的阶跃型滑坡位移预测深度学习模型
Step-like displacement prediction of landslides guided by deformation mechanism
蒋亚楠 1郑林枫 2许强 3汤明高 3朱星4
作者信息
- 1. 成都理工大学地球与行星科学学院,四川 成都 610059;成都理工大学地质灾害防治与地质环境保护国家重点实验室,四川成都 610059;四川省工业互联网智能监测及应用工程技术研究中心,四川成都 610059
- 2. 成都理工大学地球与行星科学学院,四川 成都 610059
- 3. 成都理工大学地质灾害防治与地质环境保护国家重点实验室,四川成都 610059
- 4. 成都理工大学地质灾害防治与地质环境保护国家重点实验室,四川成都 610059;四川省工业互联网智能监测及应用工程技术研究中心,四川成都 610059
- 折叠
摘要
阶跃型滑坡变形时间曲线呈阶梯状,阶跃变形量大,准确预警预报困难.针对现有模型在阶跃型滑坡快速变形阶段预测误差大的问题,提出一种机理引导下的阶跃型滑坡变形预测模型,该模型在深入分析滑坡变形机理上,结合变分模态分解开展滑坡位移和影响因子的动态响应分析,为Informer模型提供合理有效的外部影响因子输入,结合多头注意力机制和池化层,实现阶跃期时序数据关键周期信息的有效提取.本研究以三峡库区白水河滑坡为例,收集水库蓄水以来连续15年的逐月位移监测数据及同期逐天的降雨和库水位数据.试验结果表明,本文模型在阶跃型滑坡位移预测中整体预测精度较高,与主流预测模型相比,该模型对快速变形期的阶跃变形预测较为准确,预测误差较小.
Abstract
Rainfall reservoir-induced landslides in the Three Gorges Reservoir(TGR),China,exhibit distinctive step-like de-formation characteristics,involving mutation and creep states.These particular features pose a challenge for accurate early warning and prediction.Previous landslide displacement forecasting models have shown limited prediction accuracy,particular-ly when it comes to mutational displacements.The proposed prediction model in this study,based on Informer,utilizes a multi-head attention mechanism to capture temporal dependencies and incorporates pooling layers for emphasizing crucial fea-tures,enabling adaptive learning of feature weights and more effective extraction of periodic information from time series data.The Baishuihe landslide was used for case studies with monitoring data collected from July 2013 to December 2018,including monthly displacements,daily rainfall and reservoir water level.Firstly,cumulative displacement was decomposed into trend displacement and periodic displacement by the variational mode decomposition(VMD).After triggering factors selection and decomposition,the double exponential smoothing(DES)method and the Informer model are used to predict the trend and peri-odic component displacements,respectively.Finally,the predicted trend and periodic components are combined to generate the cumulative displacement prediction.Results demonstrate that the proposed model achieves impressive results with a root mean square error of 12.21 mm,a mean absolute error of 10.05 mm,and a coefficient of determination of 0.99 for the next 27 months'cumulative displacement prediction.Compared to other four mainstream models,this approach exhibits higher predic-tion accuracy,particularly in predicting the rapid deformation phase of step-like bank landslides.Consequently,it holds signifi-cant credibility and practical value in the early warning research of rainfall reservoir-induced landslides.
关键词
阶跃型滑坡/变形机理/位移预测/Informer/自注意力机制/影响因子Key words
step-like landslide/deformation mechanism/displacement prediction/Informer/attention mechanism/triggering factors引用本文复制引用
基金项目
长江生态环境保护修复联合研究二期项目(2022-LHYJ-02-0201)
国家自然科学基金(42304042)
四川省重点研发项目(2023YFS0439)
出版年
2024
NETL
NSTL
万方数据