Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Xie, Shaofeng; Liang, Yueji; Zhong, Zheng; Liu, Haifeng

doi:10.3390/ijgi6010005

Cited by 11 publications

(5 citation statements)

References 27 publications

(44 reference statements)

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“…Therefore, this proposed method has good performance in the fault diagnosis of rolling bearings. In order to further verify the superiority of EEMD over EMD and modified ensemble empirical mode decomposition (MEEMD) [42], the four groups of signals were decomposed by EMD and MEEMD respectively, and then GG clustering analysis was carried out. The results are shown in Table 4 and Figure 7.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

Hou

Gong

et al. 2020

Applied Sciences

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For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

Hou

Gong

et al. 2020

Applied Sciences

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“…With the application of remote sensing techniques, surface deformation monitoring using high-resolution satellite data has been widely used and it can well realize the monitoring over a very large area. Many related studies have been carried out on surface deformation such as deformation in volcanoes [1], glacier movement [6][7][8][9][10], landslides [4,[11][12][13], permafrost [14], land subsidence [15,16], deformation in mining areas [5,[17][18][19][20] or other geohazards detections [2,[21][22][23][24]. A variety of approaches using interferometric synthetic aperture radar (InSAR) or space-borne differential InSAR have been used for the monitoring of mining-induced displacements in many mining areas [5].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Surface Deformation of Mining Areas Using ZY-3 Stereo Imagery and DSMs

Hu,

Xu,

Zhang

et al. 2023

Remote Sensing

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Measuring surface deformation is crucial for a better understanding of spatial-temporal evolution and the mechanism of mining-induced deformation, thus effectively assessing the mining-related geohazards, such as landslides or damage to surface infrastructures. This study proposes a method of retrieving surface deformation by combining multi-temporal digital surface models (DSMs) with image homonymous features using China’s ZY-3 satellite stereo imagery. DSM is generated from three-line-array images of ZY-3 satellite using a rational function model (RFM) as the imaging geometric model. Then, elevation changes in deformation are extracted using the difference of DSMs acquired at different times, while planar displacements of deformation are calculated using image homonymous features extracted from multi-temporal digital orthographic maps (DOMs). Scale invariant feature transform (SIFT) points and line band descriptor (LBD) lines are selected as two kinds of salient features for image homonymous features generation. Cross profiles are also extracted for deformation in typical regions. Four sets of stereo imagery acquired in 2012 to 2022 are used for deformation extraction and analysis in the Fushun coalfield of China, where surface deformation is quite distinct and coupled with rising and descending elevation together. The results show that 21.60% of the surface in the study area was deformed from 2012 to 2017, while a decline from 2017 to 2022 meant that 17.19% of the surface was deformed with a 95% confidence interval. Moreover, the ratio of descending area was reduced to 6.44% between 2017 and 2022, which is lower than the ratios in other years. The slip deformation area in the west open pit mine is about 1.22 km2 and the displacement on the south slope is large, reaching an average of 26.89 m and sliding from south to north to the bottom of the pit between 2012 and 2017, but elevations are increased by an average of about 16.35 m, involving an area of about 0.86 km2 between 2017 and 2022 due to the restoration of the open pit. The results demonstrate that more quantitative features and specific surface deformation can be retrieved in mining areas by combining image features with DSMs derived from ZY-3 satellite stereo imagery.

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“…Its main advantage is that it allows the use of a binary dependent variable-the occurrence of landslides in susceptibility mapping (Yilmaz, 2009;Ozdemir and Altural, 2013;Kavzoglu et al, 2014;Chan et al, 2018;Tian et al, 2019). In addition, methodological models, such as the frequency ratio model, multivariate adaptive regression splines, the generalized summation model, the deterministic factor method, the weight of evidence, and the entropy method have also been widely used in the spatial modeling of landslide hazards (Pardeshi et al, 2013;Xu et al, 2013;Jaafari et al, 2014;Regmi et al, 2014;Conoscenti et al, 2015;Youssef et al, 2015;Ilia and Tsangaratos, 2016;Xie et al, 2017;Ma and Xu, 2019). However, researchers have found that traditional mathematical methods were not sufficient to address the problems caused by the complexity of topography, geology, and other elements associated with the occurrence of landslide hazards: more flexible nonlinear methods were generally needed.…”

Section: Open Access Edited By 1 Introductionmentioning

confidence: 99%

Landslide susceptibility analysis based on a PSO-DBN prediction model in an earthquake-stricken area

Wang

Lin

Xing

et al. 2022

Front. Environ. Sci.

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In recent years, the major geological hazard of landslides has greatly impact normal human life. Deep belief networks (DBN) is a commonly used deep learning model, and the DBN hyperparameter determination problem is the key to its application. To improve the accuracy of regional landslide susceptibility prediction, this paper introduces the particle swarm algorithm (PSO) to determine the hyperparameters of the DBN; this is applied to regional landslide susceptibility prediction. Firstly, PSO is used to optimize the hyperparameters of the DBN and obtain a set of hyperparameters with the optimal fitness function. A landslide susceptibility prediction model based on PSO-DBN is then constructed and the K-fold cross-validation method is used to determine its accuracy. The model is applied to landslide susceptibility prediction in the most impacted area of the Wenchuan earthquake to analyze the model’s accuracy. Finally, model susceptibility analysis is performed. The research results show that the final optimal model accuracy of the PSO-DBN model is 95.52%, which is approximately 28.31% and 15.35% higher than that of the logistic regression (LR) model and the common DBN model, respectively. The Kappa coefficient is 0.883, which is higher than that of the LR model. Compared with the LR model and the common DBN model, Kappa coefficient is improved by approximately 0.542 and 0.269 respectively; the area under the curve (AUC) is 0.951, which is improved by approximately 0.201 and 0.080 compared to the LR model and the common DBN model. The susceptibility of the model to the inertia factor is low, the average change in model accuracy (when the inertia factor changes by 0.1) is approximately 0.1%, and the overall stability of the model is high. The landslide susceptibility level is very high. The area includes 219 landslide points, which account for 39.2% of total landslide points. In the area with a high level of landslide susceptibility are 191 landslide points, accounting for 34.2% of total landslide points. Together, the two contain approximately 73.4% of the landslide points. This indicates that the model prediction results agree well with the spatial distribution characteristics of the landslide.

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Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

Cited by 11 publications

References 27 publications

A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

Retrieving Surface Deformation of Mining Areas Using ZY-3 Stereo Imagery and DSMs

Landslide susceptibility analysis based on a PSO-DBN prediction model in an earthquake-stricken area

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