PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect

Zhang, Lei; Shi, Bin; Zhu, Hong‐Hu; Yu, Xiong; Han, Heming; Fan, Xudong

doi:10.1007/s10346-020-01426-2

Cited by 76 publications

(26 citation statements)

References 37 publications

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“…Next, the best fitting effect is obtained in the space of the optimal decision function model, and the training sample is used to validate the analytical model results [ 15 ]. However, due to a lack of theoretical methods available to determine the penalty factor and the kernel function parameter (

), the approach for selecting

and

must be further studied [ 7 , 14 ]. In this paper, ACO was adopted to obtain the optimal parameters for the SVR model, and its performance was compared to the original SVR model and GA-SVR model.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Sensors 2020, 20, 4287 2 of 27 The support vector machine (SVM) proposed by Vapnik [10], as a popular machine learning method that offers solutions for both classification and regression problems, has been widely used in snow avalanche hazard prediction [11], earth fissure hazard prediction [12], landslide displacement prediction [13,14], etc. The support vector regression (SVR) algorithm is the regression method of SVM, which has many applications in the prediction of time series combined with time-series theory [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhang

Tang

Wen

et al. 2020

Sensors

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Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.

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), the approach for selecting

and

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Tang

Wen

et al. 2020

Sensors

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“…In recent years, the optical fiber sensing technology has shown its advantages in geological hazard monitoring and early warning [67,72,73]. The monitoring technology has been successfully applied to monitoring geological hazards such as landslides, ground collapse, ground subsidence, and ground fissures [21,[74][75][76][77][78][79][80][81]. In geological hazard monitoring, deformation of rock and soil mass and water distribution are two very important monitoring contents.…”

Section: Applications To Geo-hazards Monitoringmentioning

confidence: 99%

DFOS Applications to Geo-Engineering Monitoring

et al. 2021

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Optical fiber sensing technology has developed rapidly since the 1980s with the development of the optical fiber and fiber optical communication technology. It is a new type of sensing technology that uses light as a carrier and optical fiber as a medium to sense and transmit external signals (measurands). Distributed fiber optical sensors (DFOS) can continuously measure the external physical parameters distributed along the geometric path of the optical fiber. Meanwhile, the spatial distribution and change information of the measured physical parameters over time can be obtained. This technology has unmatched advantages over traditional point-wise and electrical measurement monitoring technologies. This paper summarizes the state-of-the-art research of the application of the distributed optical fiber sensing technology in geo-engineering in the past 10 years, mainly including the advantages of DFOS, the challenges in geo-engineering monitoring, related fundamental theoretical issues, sensing performance of the optical sensing cables, distributed optical fiber monitoring system for geo-engineering, and applications of optical fiber sensing technology in geo-engineering.

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“…The massive strain data are stored and calculated in the software and would be a database for the precise prediction of subsurface deformation if the machine learning method could be integrated into the software. A similar approach has been implemented in the landslide early warning system, e.g., Xu et al (2020) and Zhang et al (2021b). 2.…”

Section: Prospect On Fiber-optic Wireless Sensor Networkmentioning

confidence: 99%

Fiber-optic wireless sensor network using ultra-weak fiber Bragg gratings for vertical subsurface deformation monitoring

Liu

Shi

et al. 2021

Nat Hazards

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The determination of subsurface deformation is critical to understanding the subsurface dynamic processes, but most of conventional monitoring methods still have challenges in remotely obtaining detailed data. Herein, a novel fiber-optic wireless sensor network using the ultra-weak fiber Bragg gratings technique was proposed. It allows real-time remote capture of subsurface deformation along the fiber-optic cables. Such a fiber-optic measurement system was employed in a borehole to a depth of 340 m in Cangzhou, China. Strain profiles were measured monthly with a spatial resolution of 5 m and strain resolution of 1 µε, and the development of all strata deformation in the aquifer systems was calculated from January 2019 to December 2019. It turns out that the subsidence rate in this area is approximate 9 mm/a, which agrees well with the result of extensometer measurements. The significant strata compaction in the second aquifer and the third aquitard rapidly increased from May 2019 to July 2019, which could be attributed to the decline in groundwater. It is concluded that the fiber-optic wireless sensor network accurately captures subsurface deformation development and helps to better elucidate the subsurface deformation mechanism and provide solutions for the prevention and mitigation.

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PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect

Cited by 76 publications

References 37 publications

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

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

DFOS Applications to Geo-Engineering Monitoring

Fiber-optic wireless sensor network using ultra-weak fiber Bragg gratings for vertical subsurface deformation monitoring

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