Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China

Xu, Shiluo; Niu, Ruiqing

doi:10.1016/j.cageo.2017.10.013

Cited by 194 publications

(88 citation statements)

References 23 publications

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“…RNNs contain cyclic connections that make them powerful tools for modelling sequential data. In an RNN model, each unit is associated with the others in hidden layers at different time steps; the previous information stored at previously hidden layers is thus applied to the current output (see Figure A). However, traditional RNNs that are used to learn what to put in short‐term memory take too much time and sometimes even do not work at all, especially when minimum time lags between inputs and corresponding signals are long .…”

Section: Modelling Soil Cyclic Behaviour Using Lstmmentioning

confidence: 99%

“…Compared with general neural networks, connections between hidden units can be established in recurrent neural networks (RNNs), allowing the latter to retain memories of recent events. Conventional RNNs exist gradients vanishing and exploding, a long short‐term memory (LSTM) neural network as a variation of RNNs was thus developed to overcome this problem The LSTM algorithm has recently been used in practical engineering with time‐series characteristics such as the prediction of long‐term settlement of structures, hydro‐mechanical responses of multi‐permeability porous media and structural seismic response . Because soil behaviour under cyclic loading is a continuous process, the current stress‐strain status depends on the soil behaviour at previous steps and also affects the soil behaviour at the later steps.…”

Section: Introductionmentioning

confidence: 99%

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An AI‐based model for describing cyclic characteristics of granular materials

Zhang

Yin

Jin

et al. 2020

Num Anal Meth Geomechanics

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Modelling cyclic behaviour of granular soils under both drained and undrained conditions with a good performance is still a challenge. This study presents a new way of modelling the cyclic behaviour of granular materials using deep learning. To capture the continuous cyclic behaviour in time dimension, the long short-term memory (LSTM) neural network is adopted, which is characterised by the prediction of sequential data, meaning that it provides a novel means of predicting the continuous behaviour of soils under various loading paths. Synthetic datasets of cyclic loading under drained and undrained conditions generated by an advanced soil constitutive model are first employed to explore an appropriate framework for the LSTM-based model. Then the LSTM-based model is used to estimate the cyclic behaviour of real sands, ie, the Toyoura sand under the undrained condition and the Fontainebleau sand under both undrained and drained conditions. The estimates are compared with actual experimental results, which indicates that the LSTM-based model can simultaneously simulate the cyclic behaviour of sand under both drained and undrained conditions, ie, (a) the cyclic mobility mechanism, the degradation of effective stress and large deformation under the undrained condition, and (b) shear strain accumulation and densification under the drained condition. K E Y W O R D S constitutive model, cyclic loading, deep learning, liquefaction, neural network, sand

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Section: Modelling Soil Cyclic Behaviour Using Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An AI‐based model for describing cyclic characteristics of granular materials

Zhang

Yin

Jin

et al. 2020

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

show abstract

“…Recently, due to the advantages of deep learning in automatic feature extraction and high recognition rate or prediction accuracy, it has been successfully applied to speech recognition [17], action recognition [18], remaining useful life prognosis [19], traffic flow prediction [20], and other fields. As a commonly used deep learning model, the recurrent neural network (RNN) is an effective method for modeling dynamic sequences [21][22][23][24][25][26][27][28]. However, a RNN struggles to model long sequences because of gradient disappearance.…”

Section: Lstm Networkmentioning

confidence: 99%

“…To demonstrate the superiority of the proposed VMD-SLSTM network, two advanced forecasting models-the EMD-LSTM network [21] and the LSTM network [32]-are used for comparison. Figure 10 shows the forecast results of landslide displacement using the three forecasting models.…”

Section: Comparison With Other Forecasting Modelsmentioning

confidence: 99%

Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network

et al. 2019

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In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)-LSTM network, the proposed model has higher forecast accuracy.

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“…EMD has been applied to the decomposition prediction of nonlinear time series [20,21], but EMD has the effect of mode mixing and point effects [22]. The introduction of ensemble empirical mode decomposition (EEMD) [23] and variational mode decomposition (VMD) [24] has improved the mode mixing to some extent.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Yang

2018

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Abstract:The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan'an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

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Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China

Cited by 194 publications

References 23 publications

An AI‐based model for describing cyclic characteristics of granular materials

An AI‐based model for describing cyclic characteristics of granular materials

Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network

A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

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