Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

Wang, Chenhui; Wei, Guo

doi:10.3390/su15065470

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…At present, the GWO-SVR model is well applied to a variety of different prediction and detection projects. A prediction model for landslide displacement is established according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized with the gray wolf optimizer (GWO-SVR), and the results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization [63]. Owing to the serious interference from soil moisture content in the detection techniques, such as the X-ray fluorescence spectroscopy XRF method, a support vector regression SVR correction prediction model is proposed using the grey wolf optimization GWO algorithm, and the results show that the SVR nonlinear model has a better decision coefficient and smaller errors than the linear regression model [64].…”

Section: Construction Of the Screening Parameter Prediction Modelmentioning

confidence: 99%

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

et al. 2023

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The optimization of screening parameters will directly improve the screening performance of vibration screens, which has been a concern of the industry. In this work, the discrete element model of wet sand and gravel particles is established, and the vibration screening process is simulated using the discrete element method (DEM). The screening efficiency and time are used as evaluation indices, and the screening parameters including amplitude, vibration frequency, vibration direction angle, screen surface inclination, the long and short half-axis ratio of the track, feeding rate, and screen surface length are investigated. The results of an orthogonal experiment and range analysis show that the amplitude, screen surface inclination, and vibration frequency are significant factors affecting screening performance. Then, the support vector regression optimized with the grey wolf optimizer (GWO-SVR) algorithm is used to model the screening data. The screening model with excellent learning and prediction ability is obtained with the Gaussian kernel function setting. Moreover, the GWO-SVR algorithm is used to optimize the screening parameters, and the screening parameters with optimal screening efficiency and time are obtained. Furthermore, the effectiveness and reliability of the optimized model are verified using the discrete element calculation. The optimization strategy proposed in this work could provide guidance for the structural design of vibration screens and screening process optimization.

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Section: Construction Of the Screening Parameter Prediction Modelmentioning

confidence: 99%

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

et al. 2023

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“…A suitable method needs to be chosen to optimize the initial weights of the CNN model to improve the model accuracy. Compared with traditional optimization algorithms such as genetic algorithm (GA), the grey wolf optimizer (GWO) algorithm has a stronger global search capability [25][26][27]. Therefore, in order to obtain reliable landslide susceptibility evaluation results for Mangshan Mountain, the GWO algorithm is introduced to search for the best initial weights of the one-dimensional CNN (1D CNN) model.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model

Hu,

Yan

2024

Sustainability

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The Mangshan Mountain is located in the south bank of the Yellow River, which belongs to the typical loess plateau. Landslide disasters occur frequently in this region, so it is urgent to carry out the evaluation of landslide susceptibility. Therefore, this study takes Mangshan Mountain as the research object, selects 13 evaluation factors through multicollinearity diagnostic, Pearson correlation coefficient, and random forest importance analysis, and uses grey wolf optimizer (GWO) algorithm to optimize the initial weights of one-dimensional convolutional neural network model (1D CNN), so as to build a GWO-1D CNN model to carry out the evaluation of landslide susceptibility. The results show that the GWO algorithm can significantly improve the accuracy of 1D CNN model. The final accuracy of the GWO-1D CNN model reaches 0.903, and the accuracy, area under the ROC curve, and kappa coefficients increase by 0.091, 0.098, and 0.187, respectively; The percentage of area of very low, low, medium, high, and very high susceptibility areas in Mangshan Mountain is 40.2%, 23.6%, 14.1%, 12.9%, and 9.2%. The findings of this study provide scientific basis for the prevention and control of landslide disaster in Mangshan Mountain and expand the application of CNN model in the evaluation of landslide susceptibility.

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“…The determination of parameters and the quadratic penalty factor in the VMD method significantly affect the decomposition results [31]. VMD parameters are often determined through empirical judgment based on multiple tests [32]. In recent years, with the continuous development of optimization algorithms, many scholars have used them to optimize VMD parameters; they include the Genetic Algorithm (GA) [33], Grey Wolf Optimizer (GWO) [34], and Sparrow Search Algorithm (SSA) [35], etc.…”

Section: Introductionmentioning

confidence: 99%

Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination

Shen,

2023

Processes

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The accurate prediction of mining area surface deformation is essential to preventing large-scale coal mining-related surface collapse and ensure safety and daily life continuity. Monitoring subsidence in mining areas is challenged by environmental interference, causing data noise. This paper employs the Sparrow Search Algorithm, which integrates Sine Cosine and Cauchy mutation (SCSSA), to optimize variational mode decomposition (VMD) and combine multi-models for prediction. Firstly, SCSSA is employed to adaptively determine the parameters of VMD using envelope entropy as the fitness value. Subsequently, the VMD method optimized using SCSSA adaptively decomposes the original mining area subsidence data sequence into various sub-sequences. Then, SCSSA-VMD is applied to adaptively decompose the original mining subsidence data sequence into multiple sub-sequences. Meanwhile, using sample entropy, the sub-sequences are categorized into trend sequences and fluctuation sequences, and different models are employed to predict sub-sequences at different frequencies. Finally, the prediction results from different sub-sequences are integrated to obtain the final prediction of mining area subsidence. To validate the predictive performance of the established model, experiments are conducted using GNSS monitoring data from the 110801 working face of Banji Coal Mine in Bozhou. The results demonstrate the following: (1) The hybrid model enhanced the prediction accuracy and trends by decomposing the data and optimizing the parameters with VMD. It outperformed single models, reducing errors and improving predictive trends. (2) The hybrid model significantly improved the prediction accuracy for subsidence data at work surface monitoring stations. It is particularly effective at critical subsidence points, making it a valuable reference for safety in mining operations.

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Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

Cited by 7 publications

References 48 publications

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

Study on Screening Parameter Optimization of Wet Sand and Gravel Particles Using the GWO-SVR Algorithm

Evaluation of Landslide Susceptibility of Mangshan Mountain in Zhengzhou Based on GWO-1D CNN Model

Mine Surface Settlement Prediction Based on Optimized VMD and Multi-Model Combination

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