Dynamic forecast model for landslide displacement with step-like deformation by applying GRU with EMD and error correction

Meng, Yongdong; Qi, Yi; Cai, Zhenglong; Tian, Bo; Yuan, Changwei; Zhang, Xiangyu; Zuo, Qingjun

doi:10.1007/s10064-023-03247-8

Cited by 13 publications

(5 citation statements)

References 43 publications

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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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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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“…It makes up for the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, and the prediction accuracy is improved. Meng et al (2023) [24] used a dynamic hybrid model of gated recurrent unit (GRU) and error correction for landslide displacement prediction. It can not only better capture the local change of the accelerated deformation state, but also effectively reduce the extended error in the displacement prediction of long time series.…”

Section: Introductionmentioning

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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“…Meanwhile, the GRU model represents an improved model of the RNN, adept at capturing long-term dependencies in the dataset [35]. Previous studies have applied CNN for GNSS residual processing [36] and classification [37], GRU for landslide prediction [38] and multipath modeling [39], as well as CNN-GRU for forecasting wind power [40], particulate matter concentrations [41], and the pressure of a concrete dam [42]. The CNN-GRU model integrates the characteristics of CNN and GRU, which has better a performance in predictions of GNSS deformation monitoring.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Xie,

Wang,

et al. 2024

Applied Sciences

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Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

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Dynamic forecast model for landslide displacement with step-like deformation by applying GRU with EMD and error correction

Cited by 13 publications

References 43 publications

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

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

Study on Landslide Displacement Prediction Considering Inducement under Composite Model Optimization

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

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