Landslide displacement prediction model based on variational mode decomposition and MCNN-SE-GRU

IF 5.7 1区 工程技术 Q1 ENGINEERING, GEOLOGICAL
Yi Wang, Yuchen Li, Tianfeng Gu, Jiading Wang
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引用次数: 0

Abstract

Various deep learning models are employed to predict landslide displacement. However, existing deep learning prediction models do not take into account the rich multi-scale information of external triggering factors from multiple sources and the impact of each influencing factor on the degree of triggered landslide displacements. Therefore, this work applies the variational mode decomposition (VMD) theory to decompose the “step” displacement of a landslide into trend displacement, periodic displacement, and stochastic displacement. A polynomial function is used to predict the trend displacement, VMD is used to calculate the high-frequency and low-frequency components of periodic displacement and random displacement, and the main trigger factors are determined by the grey correlation degree. Then, a hybrid deep learning model based on multi-scale convolutional neural networks (MCNN) and squeeze-and-excitation networks (SENet) combined with the gated recurrent unit (GRU), called MCNN-SE-GRU, is proposed to predict the periodic displacement and random displacement. In the model, the MCNN block is designed to extract three convolutional kernels of different scales to form receptive fields of different sizes and obtain global and local trigger factor characteristics. The SE block builds dependencies between channels by learning global and local information. By dynamically adjusting the weights of the channels of each feature, important features are reinforced, and non-important features are suppressed. Then, the GRU block is used for feature extraction of dependencies between temporal data. Finally, the feature fusion block is used to stitch the multi-featured vectors and linear regression is used to calculate the final displacement prediction values. Taking the Baijiabao Landslide in the Three Gorges of China as an example, data from two monitoring points with the maximum and minimum displacement changes were selected to reflect the sensitivity of model predictions and a comparative analysis was conducted with four mainstream models, four latest prediction models, and three different combinations of models. The results show that the root-mean-square error (RMSE) of MCNN-SE-GRU was 2.50 mm and 2.33 mm, respectively. Compared with the mainstream models, the prediction performance of MCNN-SE-GRU was at least improved by 66.44 % and 76.05%. Compared with the latest models, the prediction performance of MCNN-SE-GRU was at least enhanced by 23.31% and 11%. It has been verified that our method can effectively suppress the random fluctuations of the prediction results during the creeping period of landslide displacement and accurately predict when a steplike deformation occurs, providing a more effective means of predicting the risk warning during the intense deformation period of landslides.

基于变分模态分解和MCNN-SE-GRU的滑坡位移预测模型
各种深度学习模型被用来预测滑坡位移。然而,现有的深度学习预测模型没有考虑到来自多个来源的丰富的外部触发因素的多尺度信息,以及每个影响因素对触发滑坡位移程度的影响。因此,本文运用变分模态分解(VMD)理论将滑坡的“阶跃”位移分解为趋势位移、周期位移和随机位移。采用多项式函数预测趋势位移,采用VMD计算周期位移和随机位移的高频和低频分量,并通过灰色关联度确定主要触发因素。然后,提出了一种基于多尺度卷积神经网络(MCNN)和挤压激励网络(SENet)结合门控循环单元(GRU)的混合深度学习模型MCNN- se -GRU,用于预测周期位移和随机位移。在模型中,MCNN块被设计为提取三个不同尺度的卷积核,形成不同大小的感受场,获得全局和局部触发因子特征。SE块通过学习全局和局部信息来构建通道之间的依赖关系。通过动态调整每个特征的信道权值,增强重要特征,抑制非重要特征。然后,使用GRU块对时态数据之间的依赖关系进行特征提取。最后,利用特征融合块对多特征向量进行拼接,利用线性回归计算最终的位移预测值。以三峡白家宝滑坡为例,选取位移变化最大和最小的两个监测点的数据,反映模型预测的敏感性,并与4种主流模型、4种最新预测模型和3种不同的模型组合进行对比分析。结果表明,MCNN-SE-GRU的均方根误差(RMSE)分别为2.50 mm和2.33 mm。与主流模型相比,MCNN-SE-GRU的预测性能至少提高了66.44%和76.05%。与最新模型相比,MCNN-SE-GRU的预测性能至少提高了23.31%和11%。实践证明,该方法能够有效地抑制滑坡位移蠕变期预测结果的随机波动,准确预测发生阶梯状变形时的情况,为滑坡剧烈变形期的风险预警预测提供了更有效的手段。
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来源期刊
Acta Geotechnica
Acta Geotechnica ENGINEERING, GEOLOGICAL-
CiteScore
9.90
自引率
17.50%
发文量
297
审稿时长
4 months
期刊介绍: Acta Geotechnica is an international journal devoted to the publication and dissemination of basic and applied research in geoengineering – an interdisciplinary field dealing with geomaterials such as soils and rocks. Coverage emphasizes the interplay between geomechanical models and their engineering applications. The journal presents original research papers on fundamental concepts in geomechanics and their novel applications in geoengineering based on experimental, analytical and/or numerical approaches. The main purpose of the journal is to foster understanding of the fundamental mechanisms behind the phenomena and processes in geomaterials, from kilometer-scale problems as they occur in geoscience, and down to the nano-scale, with their potential impact on geoengineering. The journal strives to report and archive progress in the field in a timely manner, presenting research papers, review articles, short notes and letters to the editors.
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