Rainfall data feature extraction and its verification in displacement prediction of Baishuihe landslide in China

Liu, Yong; Liú, Dan; Qin, Zhimeng; Liu, Fengbo; Liu, Lanbo

doi:10.1007/s10064-015-0847-1

Cited by 17 publications

(8 citation statements)

References 27 publications

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“…Miao et al also proposed that the displacement of the Baishuihe landslide changed stepwise at the rainfall-concentrated months from May to October of every year through a multiyear displacement analysis [33]. e multiyear displacement monitoring data also indicated that the landslide has cumulated a spectacular displacement (approximately 1∼2 m) at the intermediate section by stages since the first impoundment of the ree Gorges Reservoir in 2007, but it remains stable [30][31][32][33] because of the toe resistance result from the gentle slope of the interface of the sliding mass and the bedrock.…”

Section: Results and Comparisonmentioning

confidence: 99%

“…It produced remarkable deformation since the first impoundment of the ree Gorges Reservoir in 2003 and has a great influence on the shipping safety of Yangtz River. e multiyear timedisplacement data of the landslide surface obtained from GPS monitoring exhibited a step-like pattern, which aroused much research attention [30][31][32][33]. However, most previous research studies aimed at predicting the deformation behavior and paid little attention to the engineering geological model and landslide mechanism.…”

Section: Case Study: the Baishuihe Landslidementioning

confidence: 99%

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Study on the Spatially Variable Saturated Hydraulic Conductivity and Deformation Behavior of Accumulation Reservoir Landslide Based on Surface Nuclear Magnetic Resonance Survey

Zhang

Xiahou

Tang

et al. 2018

Advances in Civil Engineering

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Saturated hydraulic conductivity (Ks) is spatially variable in accumulation landslide sites that exert significant effort onto landslide seepage and deformation behavior. To better understand spatial variability and the effect of Ks on the slide mass of an accumulation landslide, this study introduced the surface nuclear magnetic resonance (SNMR) technology to study a representative reservoir accumulation landslide field in the Three Gorges Reservoir area (TGRA), the Baishuihe landslide, to obtain a series of relative reliable spatial measurements of Ks effectively on the basis of calibration in terms of the field tests measurements. The estimated Ks values were distributed log-normally for the overall landslide mass site with a wide range of 3.00 × 10−6∼7.80 × 10−3 cm/s, which reaches about 3 orders of magnitude. Variogram analysis indicated that the Ks values have the range (A) of 295.89 m and 65.56 m for the overall site and major cross-sectional analysis, respectively. A finite-element seepage-stress analysis associated with a Kriging-interpolated spatial Ks variable calculation model based on the best-fitted theoretical variogram was subsequently performed to study the seepage and deformation behavior of the landslide. The available monitored data and simulated results of the finite-element seepage-stress analysis indicated that the Baishuihe landslide is a progressive landslide, and the main factor influencing the deformation is rainfall and reservoir water fluctuation. This study provides an unconventional framework for studying the heterogeneous geomaterial and contributes to a better understanding of the spatial variation of the hydraulic property of accumulation reservoir landslides at a field scale.

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Section: Results and Comparisonmentioning

confidence: 99%

Section: Case Study: the Baishuihe Landslidementioning

confidence: 99%

Study on the Spatially Variable Saturated Hydraulic Conductivity and Deformation Behavior of Accumulation Reservoir Landslide Based on Surface Nuclear Magnetic Resonance Survey

Zhang

Xiahou

Tang

et al. 2018

Advances in Civil Engineering

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show abstract

“…The data-based models are more popular than physical models (Corominaset al 2005) because of simple process and accurate prediction. Recently, a variety of Machine Learning (ML) models have been applied in landslide spatial and temporal prediction, such as Artifical Neural Network (ANN) (Du et al 2013;Liu et al 2016), Support Vector Machine (SVM) (Wu et al 2016;Zhu et al 2017), Decision Tree (Krkač et al 2017;Ma et al 2017), Extreme Learning Machine (ELM) (Cao et al 2016;Vasu and Lee 2016;Huang et al 2017), and so on.…”

Section: Introductionmentioning

confidence: 99%

Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method

et al. 2018

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Landslide displacement prediction is an essential component for developing landslide early warning systems. In the Three Gorges Reservoir area (TGRA), landslides experience step-like deformations (i.e. periods of stability interrupted by abrupt accelerations) generally from April to September due to the influence of precipitation and reservoir scheduled level variations. With respect to many traditional machine learning techniques, two issues exist relative to displacement prediction, namely the random fluctuation of prediction results and inaccurate prediction when step-like deformations take place. In this study, a novel and original prediction method was proposed by combining the Wavelet Transform (WT) and Particle Swarm Optimization-Kernel Extreme Learning Machine (PSO-KELM) methods, and considering the landslide causal factors. A typical landslide with a step-like behavior, the Baishuihe landslide in TGRA, was taken as a case study. The cumulated total displacement was decomposed into trend displacement, periodic displacement (controlled by internal geological conditions and external triggering factors respectively), and noise. The displacement items were predicted separately by multi-factor PSO-KELM considering various causal factors, and the total displacement was obtained by summing them up. An accurate prediction was achieved by the proposed method, including the step-like deformation period. The performance of the proposed method was compared with the multi-factor Extreme Learning Machine (ELM), Support Vector Regression (SVR), Backward Propagation Neural Network (BPNN), and single-factor PSO-KELM. Results show that the PSO-KELM outperforms other models, and the prediction accuracy can be improved by considering causal factors.

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“…Secondly, external factors such as rainfall (Priest et al, 2011;Bernardie et al, 2015;Liu et al, 2016) and fluctuation of water level (Ashland et al, 2006;Huang et al, 2017) will also change landslide stability. But for now only a few literatures mentioned real-time landslide stability (Montrasio et al, 2011;Chen et al, 2014).…”

mentioning

confidence: 99%

State fusion entropy for real-time and site-specific analysis of landslide stability changing regularities

Liu¹,

Qin²,

Hu³

et al. 2017

Preprint

Self Cite

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Abstract. Stability analysis is of great significance to landslide hazard prevention, especially the real-time stability. However, many existing stability analysis methods are difficult to analyse the real-time landslide stability and its changing regularities in a uniform criterion due to the unique landslide geological conditions. Based on the relationship between displacement monitoring data, deformation states and landslide stability, a state fusion entropy method is herein proposed to derive landslide instability through a comprehensive multi-attribute entropy analysis of deformation states which are defined by a proposed 10 joint clustering method combining K-means and cloud model. Taking Xintan landslide as the detailed case study, cumulative state fusion entropy presents an obvious increasing trend after the landslide entered accelerative deformation stage and historical maxima match highly with the evolutionary stages of Xintan landslide in key time nodes. Reasonable results are also obtained in its application to several other landslides in the Three Gorges Reservoir in China. Combined with field survey, state fusion entropy may serve as a novel index for judging landslide stability changing regularities and for landslide early 15 warning.

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Rainfall data feature extraction and its verification in displacement prediction of Baishuihe landslide in China

Cited by 17 publications

References 27 publications

Study on the Spatially Variable Saturated Hydraulic Conductivity and Deformation Behavior of Accumulation Reservoir Landslide Based on Surface Nuclear Magnetic Resonance Survey

Study on the Spatially Variable Saturated Hydraulic Conductivity and Deformation Behavior of Accumulation Reservoir Landslide Based on Surface Nuclear Magnetic Resonance Survey

Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method

State fusion entropy for real-time and site-specific analysis of landslide stability changing regularities

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