Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network

Xing, Yin; Yue, Jianping; Chen, Chuang

doi:10.1109/access.2019.2961295

Cited by 42 publications

(16 citation statements)

References 30 publications

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“…Taking the Baishuihe landslide located in the Three Gorges Reservoir area as a case study, the proposed model showed satisfactory performance. Another study [29] investigated a coupling prediction model based on the double moving average (DMA) method and the LSTM network to improve the accuracy of landslide displacement point prediction and quantify the uncertainties associated with the predicted values.…”

Section: Related Workmentioning

confidence: 99%

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

et al. 2022

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Predicting slope deformation prediction is crucial for early warning of slope failure, preventing damage to properties, and saving human lives. However, in practice, equipment maintenance causes discontinuity in the displacement data, and the traditional prediction models based on deep networks do not perform well in this case. To solve the problem of prediction accuracy in case of discontinuous and inadequate data, we propose a combined displacement prediction model that integrates the bidirectional gated recurrent unit (Bi-GRU), attention mechanism, and transfer learning. The Bi-GRU is employed to extract the forward and backward characteristics of displacement series, and the attention mechanism is utilized to give different weights to the extracted information so as to highlight the critical information. Transfer learning is used to guarantee prediction accuracy in case of discontinuous and limited data. The model is then employed to predict the slope displacement of the JinYu Cement Plant in China. Finally, the modeling results excellently agree with measured displacement, especially in case of insufficient sample data.

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Section: Related Workmentioning

confidence: 99%

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

et al. 2022

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“…With the improvement of computer calculating ability and the development of machine learning and artificial intelligence, the disaster-prediction ability of landslides has been greatly improved. At present, the commonly used models for landslide displacement prediction include the backpropagation neural network (BPNN) [14,15,23], support-vector regression (SVR) [18,[24][25][26][27][28], extreme learning machine (ELM) [29][30][31][32], kernel extreme learning machine (KELM) [14,23,33], long short-term memory (LSTM) [25,[34][35][36][37], decision tree [38], and so on. Moreover, many algorithms are used to optimize the parameters of the prediction models, including the genetic algorithm (GA) [28,39,40], particle swarm optimization (PSO) [16,26,28,29,41,42], fruit fly optimization algorithm (FOA) [18], grey wolf optimizer (GWO) [15], and so on.…”

Section: Introductionmentioning

confidence: 99%

Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs

Xu¹,

Jiang²,

Yang³

2022

Applied Sciences

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In order to promptly evacuate personnel and property near the foot of the landslide and take emergency treatment measures in case of sudden danger, it is very necessary to select suitable forecasting methods for conduct short-term displacement predictions in the slope-sliding process. In this paper, we used Python to develop the landslide displacement-prediction method based on the eXtreme Gradient Boosting (XGBoost) algorithm, and optimized the hyperparameters through a genetic algorithm to solve the problem of insufficient short-term displacement-prediction accuracy for landslides. We compared the deviation, relative error (RE) and median of RE of predicted values obtained using XGBoost, SVR and RNNs, and the actual value of landslide displacement. The results show that the accuracies of slope displacement prediction using XGBoost and SVR are very high, and that using RNNs is very low during the sliding process. For large displacement values and small numbers of samples, the displacement-prediction effect of XGBoost algorithm is better than that of SVR and RNNs in the sliding process of landslide. There are generally only fewer data samples collected during the landslide sliding process, so RNNs is not suitable for displacement prediction in this scenario. If the number of data samples is large enough, using RNNs to predict the long-term displacement of the slope may also have a much higher accuracy.

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“…Ma et al (2018) proposed using a bootstrapbased ELM to construct PIs for landslide incremental displacement. Xing et al (2019) revised the structure of long short-term memory (LSTM) network to generate probabilistic forecasting of landslide displacement and produced highquality PIs for future displacement. Jiang et al (2021) proposed a hybrid grey wolf optimizer to optimize the ELM structure for PI construction and achieved improved probabilistic prediction performance using field data.…”

Section: Introductionmentioning

confidence: 99%

Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

Yang

Feng²,

Wan³

et al. 2022

Front. Earth Sci.

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Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide hazards. In this study, a probabilistic landslide displacement forecasting model based on the quantification of epistemic uncertainty is proposed. In particular, the displacement forecasting problem is cast as a time-series regression problem with limited training samples and must be solved by statistical inference. The epistemic uncertainty of the landslide displacement series is depicted by the statistical properties of the function space constituted by the nonlinear mappings generated by the sparse Gaussian process regression. Data for our study was collected from the study area located in northwestern China. Other state-of-the-art probabilistic forecasting models have also been utilized for comparative analysis. The experimental results confirmed the superiority of the sparse Gaussian process in the modeling of landslide displacement series in terms of forecasting accuracy, uncertainty quantification, and robustness to overfitting.

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Interval Estimation of Landslide Displacement Prediction Based on Time Series Decomposition and Long Short-Term Memory Network

Cited by 42 publications

References 30 publications

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs

Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

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