A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area

Zhang, Junrong; Tang, Huiming; Wen, Tao; Ma, Junwei; Tan, Qinwen; Xia, Ding; Liu, Xiao; Zhang, Yongquan

doi:10.3390/s20154287

Cited by 34 publications

(17 citation statements)

References 46 publications

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“…In the present study, 124 measurements from October 2006 to January 2017 were used as the training set for the prediction model, and 21 measurements from February 2017 to October 2018 were treated as testing data. According to previous research on landslide displacement prediction [46,47], the trend components are mainly controlled by internal geological conditions and can be perfectly predicted by polynomial regression fitting. In contrast, the periodic component is mainly controlled by external triggering factors, such as rainfall intensity and reservoir fluctuation.…”

Section: Individual Predictionmentioning

confidence: 99%

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

Niu

Wang

et al. 2021

Applied Sciences

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As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of “decomposition and ensemble”, a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction.

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Section: Individual Predictionmentioning

confidence: 99%

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

Niu

Wang

et al. 2021

Applied Sciences

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“…Zhang et al pointed out that the CEEMD method combined with a t-test can obtain the high-frequency and low-frequency components from related factors such as rainfall and the reservoir water level through a fine-to-coarse reconstruction [25]. Moreover, according to the time series theory, the landslide displacement can be separated into a trend term and a periodic term by methods presented in the Introduction section.…”

Section: Data Preprocessing With Ceemdmentioning

confidence: 99%

“…Miao et al [24] adopted a variety of algorithms to optimize the SVR model and achieved a good application effect in the prediction of Baishuihe landslide displacement. Zhang et al [25] made comparisons of the predictive capability of the SVR model optimized by ACO and GA and found the advantage of ACO-SVR with the consideration of the inducing factors' frequency component. At present, the application of optimization algorithms on SVR-based landslide prediction model parameter optimization is limited.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, many other statistical methods such as maximal information coefficient (MIC) [23] and mean influence value (MIV) [44] have also been tried for this purpose. Zhang et al [25] found that, as a similarity measuring method of time series, dynamic time warping (DTW) can be employed and works well in optimal input parameters selection of the SVR model. However, the DTW has the limitation of insensitive to the noise of the time series.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

Zhang

Tang

Tannant

et al. 2021

Sensors

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With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

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“…To improve empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) have been proposed, and obtained a lot of applications [12][13][14][15]. Zhang et al [16] used CEEMD and t-test to reconstruct the time series of rainfall, reservoir water level and other inducing factors into highfrequency components and low-frequency components respectively. The ant colony optimization based support vector machine regression was used to predict TGRA.…”

Section: Introductionmentioning

confidence: 99%

Combine Assembly Quality Detection Based on Multi-Entropy Data Fusion and Optimized LSSVM

Zhao

Zhang

et al. 2021

IEEE Access

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To solve the problems related to the complex structures, multiple parts, and imperceptible assembly quality of combines, this paper compares the performance of the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and information entropy features in the detection data of combine assembly quality, and proposes a vibration detection method of combine assembly quality detection based on multi-entropy feature fusion and optimized least squares support vector machine. Firstly, the vibration signals of the combine are decomposed by various adaptive algorithms, and intrinsic modal function (IMF) components are obtained. Three entropy features are extracted from the components of the modes. The features are visualized by t-distributed stochastic neighbor embedding (t-SNE), and the performance of these entropy features in the combine assembly quality detection is analyzed. Secondly, a feature extraction method based on information entropy fusion is proposed. The optimized kernel principal component analysis (KPCA) is used to fuse and reduce the dimension of the entropy features, and form the fusion features. Finally, the extracted features are imported into optimization least squares support vector machine (LSSVM) model for training to judge the working state and assembly quality problem type of combine. The results show that the accuracy of using the unfused entropy features is 82.5%, the accuracy of the fused features is 87.9%. After WOA optimization, the accuracy of the final classification reaches 92.1%, which is better than 89.6% by GA and 90.5% by PSO. It shows that the method proposed in this paper can accurately identify combine problems with different conditions and can be applied to combine assembly quality detection.

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A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area

Cited by 34 publications

References 46 publications

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction

A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm

Combine Assembly Quality Detection Based on Multi-Entropy Data Fusion and Optimized LSSVM

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