Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China

Ling, Qing; Zhang, Qin; Zhang, Jing; Kong, Ling‐Bin; Zhang, Weiqi; Zhu, Li

doi:10.1007/s11069-021-04713-w

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…In this work, landslide displacements are decomposed according to VMD theory, determining the explicit physical meaning of each component. In addition, the VMD theory, which has good adaptive ability, can be used to decompose the displacement based on the actual situation of the landslide [32,33]. Therefore, the trend, period and random displacements of landslides are well extracted in this study.…”

Section: Discussionmentioning

confidence: 99%

“…The EMD achieves a thorough decomposition of the original displacement of the landslide, but it also suffers from modal confounding and computational inefficiency [29]. In recent years, several researchers have used variational mode decomposition (VMD) combined with artificial intelligence algorithms to achieve accurate prediction of landslide displacement [32,33]. The VMD can select the number of features of displacement decomposition according to the data size and type, which can determine the physical significance of each feature component.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Wei

2023

Sustainability

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Accurate prediction of landslide displacement is an effective way to reduce the risk of landslide disaster. Under the influence of periodic precipitation and reservoir water level, many landslides in the Three Gorges Reservoir area underwent significant displacement deformation, showing a similar step-like deformation curve. Given the nonlinear characteristics of landslide displacement, a prediction model is established in this study according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized by gray wolf optimizer (GWO-SVR). First, the original data are decomposed into trend, periodic and random components by VMD. Then, appropriate influential factors are selected using the grey relational degree analysis (GRDA) method for constructing the input training data set. Finally, the sum of the three displacement components is superimposed as the total displacement of the landslide, and the feasibility of the model is subsequently tested. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as an example, the accuracy of the model is verified using the long time-series monitoring data. The results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization. Therefore, this model can be used for the predict the accuracy of names and affiliations ion of landslide displacement in the Three Gorges Reservoir area.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

Wei

2023

Sustainability

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“…Therefore, studying the optimal values for k and α is crucial. Empirical values are typically used for other parameters [42].…”

Section: Scssa-vmdmentioning

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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“…The maximal information coefficient (MIC) method [54,55] can measure the strength of both linear and nonlinear associations between data, allowing for the discovery of diverse types of relationships among different types of load data. Therefore, in this study, the MIC is used to analyze the spatiotemporal characteristics of multiple systems and to construct a high-dimensional feature matrix for model inputs.…”

Section: Maximal Information Coefficient Analysismentioning

confidence: 99%

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

Wu,

Ma,

Alharbi

et al. 2023

Sustainability

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Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve the accuracy of short-term load forecasting, this paper proposes a multi-load forecasting method for integrated energy systems based on the Isolation Forest and dynamic orbit algorithm. First, a high-dimensional data matrix is constructed using the sliding window technique and the outliers in the high-dimensional data matrix are identified using Isolation Forest. Next, the hidden abnormal data within the time series are analyzed and repaired using the dynamic orbit algorithm. Then, the correlation analysis of the multivariate load and its weather data is carried out by the AR method and MIC method, and the high-dimensional feature matrix is constructed. Finally, the prediction values of the multi-load are generated based on the TCN-MMoL multi-task training network. Simulation analysis is conducted using the load data from a specific integrated energy system. The results demonstrate the proposed model’s ability to significantly improve load forecasting accuracy, thereby validating the correctness and effectiveness of this forecasting approach.

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Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China

Cited by 13 publications

References 26 publications

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

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

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

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

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