A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression

Ma, Junwei; Wang, Yankun; Niu, Xiaoxu; Jiang, Sheng; Liu, Zhiyang

doi:10.1007/s00477-022-02183-5

Cited by 58 publications

(43 citation statements)

References 57 publications

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“…. , x (0) n ) and perform a cumulative generation (1-AGO) on the original sequence to obtain a new sequence x (1) � x (1) 1 , x (1) 2 , . .…”

Section: Grey Verhulst Modelmentioning

confidence: 99%

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Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Hou

2022

Scientific Programming

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Accurate prediction of aircraft failure rate can improve flight safety and spare parts supply efficiency and effectively provide good maintenance and maintenance decisions and health management guidance. In order to achieve accurate prediction of non-linear and non-stationary aircraft failure rate, an aircraft failure rate prediction method based on the fusion of complementary ensemble empirical mode decomposition (CEEMD) and combined model is proposed. Firstly, the complementary set empirical mode is used to decompose the failure rate into multiple components with different frequencies, then the integrated moving average autoregressive model (ARIMA) model and grey Verhulst model are selected to predict different components, the entropy weight method is used to solve the coefficients of the combined model, and finally the prediction results of each prediction model are multiplied by their respective weight coefficients to obtain the final prediction results. The experiment was carried out by taking the actual case application of the failure rate data of the aircraft fuel control system as an example. Seven evaluation functions are used as evaluation criteria to evaluate the performance of the combined model. Experimental results show that the developed combined model is better than other models such as sum of squared error (SSE) and mean absolute error (MAE), which can significantly improve the prediction accuracy of aircraft failure rate. It is proved that the model can improve the accuracy and effectiveness of aircraft failure rate prediction. At the same time, the stability of the model has certain advantages over other models and has a good application prospect.

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“…. , x (0) n ) and perform a cumulative generation (1-AGO) on the original sequence to obtain a new sequence x (1) � x (1) 1 , x (1) 2 , . .…”

Section: Grey Verhulst Modelmentioning

confidence: 99%

“…As one of the core and key technologies in many elds, prediction technology has been widely used, such as in the elds of geology [1][2][3][4], transportation [5], water conservancy [6], and wind energy [7][8][9][10][11][12][13][14][15][16]. It plays an important supporting role in decision making and management.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Hou

2022

Scientific Programming

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“…Time series analysis is a highly applied branch of probability statistics, with mathematical tools and theories used in many fields, such as finance and economics, meteorology and hydrology, signal processing and mechanical vibration [ 53 , 54 ]. Although the analysis of long-term trends and cyclical fluctuations control the basic style of time series movements, it is after all not the whole picture of time series movements, and it is more reasonable and superior to use the theory of stochastic processes and statistical theory to examine the time series of long-term trends, seasonal variations and other factors that act together.…”

Section: Impacts Of Landslide Formationmentioning

confidence: 99%

Slope with Predetermined Shear Plane Stability Predictions under Cyclic Loading with Innovative Time Series Analysis by Mechanical Learning Approach

Jiang

et al. 2022

Sensors

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We propose a mechanical learning method that can be used to predict stability coefficients for slopes where slopes with predetermined shear planes are subjected to cyclic seismic loads under undrained conditions. Firstly, shear tests with cyclic loading of different parameters were simulated on designated slip zone soil specimens, in which the strain softening process leading to landslide occurrence was closely observed. At the same time, based on the limit equilibrium analysis of the Sarma method, the variation of slope stability coefficients under different cyclic loads was investigated. Finally, a Box–Jenkins’ modeling approach is used to predict the data from the time series of slope stability coefficients using a mechanical learning approach. The simulation results show that (1) reduction in coordination number can be an accurate indicator of the level of strain softening and evolutionary processes; (2) the gradual reduction of shear stress facilitates the soil strain softening process, while different cyclic loading stress amplitudes will result in rapid penetration or non-penetration of the fracture zone by means of particulate flow. Although the confining pressure of the slip zone soil can inhibit the increase of fractures, it has a limited inhibitory effect on strain softening; (3) based on field observations of the slope stability factor and stress field, two possible landslide triggering mechanisms are described. (4) Mechanical learning of time series can accurately predict the changing pattern of stability coefficients of slopes without loading. This study establishes a potential bridge between the geological investigation of landslides and the theoretical background of landslide stability coefficient prediction.

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“…It is clear from the above analysis that despite the extensive use of experimental and analytical methods in the literature, numerical simulation analyses cannot be neglected because they can provide valuable information on the response details of structural members with more complex material properties 22 – 24 . Recently, many scholars have used numerical simulation software such as ABAQUS, LS-DYNA, and AUTODYN to study the effect of blast loading on pipelines, and more detailed dynamic response parameters were obtained, which could not be obtained experimentally 25 – 27 .…”

Section: Introductionmentioning

confidence: 99%

Theoretical analysis of the deformation for steel gas pipes taking into account shear effects under surface explosion loads

Jiang

et al. 2022

Sci Rep

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Ground blast loads are of great importance to the safe operation of steel and gas pipelines, and the results obtained from traditional theoretical formulas for pipeline safety prediction are in error with the actual measured data. In this paper, full-size field tests and corresponding numerical simulations are carried out using Timoshenko beam theory and explosion stress wave theory, which consider shear effects. At the same time, combined with the theory of foundation stiffness and pipeline stiffness flexibility ratio, a modified theoretical model is obtained in line with the actual conditions of the site, which can accurately calculate the deformation and displacement of pipeline underground explosion load, and greatly reduce the error of theoretical prediction results. The innovation of the research results in this paper is that the theoretical stress in the Timoshenko beam can be replaced by the circumferential strain. On the other hand, the modified theoretical solution can obtain the critical weight of explosives to prevent pipeline damage at different buried depths. It provides a theoretical basis for the protection of pipelines’ underground blast loads and provides research ideas for the safe protection and design of pipelines.

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A comparative study of mutual information-based input variable selection strategies for the displacement prediction of seepage-driven landslides using optimized support vector regression

Cited by 58 publications

References 57 publications

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Slope with Predetermined Shear Plane Stability Predictions under Cyclic Loading with Innovative Time Series Analysis by Mechanical Learning Approach

Theoretical analysis of the deformation for steel gas pipes taking into account shear effects under surface explosion loads

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