Using time series analysis and dual-stage attention-based recurrent neural network to predict landslide displacement

Bai, Dongxin; Lu, Guangyin; Zhu, Z. Q.; Tang, Junwu; Fang, Ji; Wen, Aixiang

doi:10.1007/s12665-022-10637-w

Cited by 15 publications

(5 citation statements)

References 59 publications

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“…The evolution of landslide surface displacement deformation over time is a complex outcome influenced by multiple internal and external factors, superimposed on each other [27]. Exploring the dynamic relationships among various influencing factors and landslide deformation in intricate displacement sequence data is a challenging task [28].…”

Section: Time Series Additive Modelmentioning

confidence: 99%

Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization

Ye,

Liu,

Xie

et al. 2024

Electronics

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The precise extraction of displacement time series for complex landslides poses significant challenges, and conventional landslide prediction models often overlook the deformation impacts of displacement triggers. To address this, we introduce a novel composite model tailored for predicting landslide displacement. This model employs Variational Mode Decomposition (VMD) to isolate each displacement component, with optimization achieved through the groupwise coupling algorithm. Subsequently, Grey correlation analysis (GRA) is applied to quantitatively assess the dynamic correlations between various triggering factors and landslide displacement. This analysis informs the construction of a feature set predicated on these correlation factors. Integrating the time-series VMD module into the standard Transformer architecture facilitates the prediction of landslide displacement. This integration allows for the extraction of critical time-evolution features associated with the displacement components. Ultimately, the predicted displacements are aggregated and reconstructed. We validate our model using the Bazimen landslide case study, analyzing displacement monitoring data from 1 January 2007, to 31 December 2012. The values of the root mean square error and the mean absolute percentage error were 1.86 and 4.85, respectively. This model offers a more nuanced understanding of the multifaceted causes and evolutionary dynamics underpinning landslide displacement and deformation, thereby markedly enhancing prediction accuracy.

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Section: Time Series Additive Modelmentioning

confidence: 99%

Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization

Ye,

Liu,

Xie

et al. 2024

Electronics

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“…As depicted in Figure 3b, a 1D SwinT block comprises two consecutive SwinT layers. The first one encompasses a standard 1D window multihead self-attention module (WMSA) and the latter one comprises a 1D shifted window MSA (SW-MSA), both followed by a set of LayerNorm (LN), two-layer multilayer perceptron (MLP) with Gaussian error linear unit nonlinearity [32,33], and residual connection [34,35] modules. With the shifted window partitioning approach, the two consecutive SwinT layers are computed as follows:…”

Section: Neural Network Architecturementioning

confidence: 99%

Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements

Liu,

Wang,

et al. 2023

Remote Sensing

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Despite demonstrating exceptional inversion production for synthetic data, the application of deep learning (DL) inversion methods to invert realistic magnetotelluric (MT) measurements, which are inevitably contaminated by noise in acquisition, poses a significant challenge. Hence, to facilitate DL inversion for realistic MT measurements, this work explores developing a noise-robust MT DL inversion method by generating targeted noisy training datasets and constructing a physics-informed neural network. Different from most previous works that only considered the noise of one fixed distribution and level, we propose three noise injection strategies and compare their combinations to mitigate the adverse effect of measurement noise on MT DL inversion results: (1) add synthetic relative noise obeying Gaussian distribution; (2) propose a multiwindow Savitzky–Golay (MWSG) filtering scheme to extract potential and possible noise from the target field data and then introduce them into training data; (3) create an augmented training dataset based on the former two strategies. Moreover, we employ the powerful Swin Transformer as the backbone network to construct a U-shaped DL model (SwinTUNet), based on which a physics-informed SwinTUNet (PISwinTUNet) is implemented to further enhance its generalization ability. In synthetic examples, the proposed noise injection strategies demonstrate impressive inversion effects, regardless of whether they are contaminated by familiar or unfamiliar noise. In a field example, the combination of three strategies drives PISwinTUNet to produce considerably faithful reconstructions for subsurface resistivity structures and outperform the classical deterministic Occam inversions. The experimental results show that the proposed noise-robust DL inversion method based on the noise injection strategies and physics-informed DL architecture holds great promise in processing MT field data.

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“…According to the idea of time series addition, cumulative landslide displacement can be decomposed into subsequences reflecting different characteristics, which usually include trend displacement, periodic displacement and random displacement [17,18]. The commonly used decomposition methods include wavelet analysis (WA) [19], empirical modal decomposition (EMD) [20,21], ensemble empirical mode decomposition (EEMD) [22,23] and variational modal decomposition (VMD) [24,25]. The VMD shows better decomposition in landslide cumulative displacement decomposition.…”

Section: Introductionmentioning

confidence: 99%

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

Xie,

Huang

2024

Mathematics

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Landslide displacement prediction is of great significance for the prevention and early warning of slope hazards. In order to enhance the extraction of landslide historical monitoring signals, a landslide displacement prediction method is proposed based on the decomposition of monitoring data before prediction. Firstly, based on the idea of temporal addition, the sparrow search algorithm (SSA) coupled with the variational modal decomposition (VMD) algorithm is used to decompose the total landslide displacement into trend item, periodic item and random item; then, the displacement values of the subitems are fitted by using the long and short-term memory (LSTM) neural network, and the predicted cumulative landslide displacement is obtained by adding up the predicted values of the three subsequences. Finally, the historical measured data of the Shuping landslide is taken as an example. Considering the effects of seasonal rainfall and reservoir water level rise and fall, the displacement of this landslide is predicted, and the prediction results of other traditional models are compared. The results show that the landslide displacement prediction model of SSA-VMD coupled with LSTM can predict landslide displacement more accurately and capture the characteristics of historical signals, which can be used as a reference for landslide displacement prediction.

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Using time series analysis and dual-stage attention-based recurrent neural network to predict landslide displacement

Cited by 15 publications

References 59 publications

Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization

Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization

Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

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