Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors

Cao, Ying; Yin, Kunlong; Alexander, David; Zhou, Chao

doi:10.1007/s10346-015-0596-z

Cited by 125 publications

(61 citation statements)

References 21 publications

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“…In the Three Gorges Reservoir region, landslides are the main type of geohazard, and they cause critical harm to individuals and property each year (Du et al, 2013;Yao et al, 2013;Lian et al, 2014;Cao et al, 2016). The displacement prediction of landslides is a major focus in the field of landslide research (Sassa et al, 2009;Du et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.

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Section: Introductionmentioning

confidence: 99%

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Wen

Tang

Wang

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…Xie et al [104] Laowuji, China Rainfall, toe excavation Total Station LSTM Bossi and Marcato [105] Passo della Morte, Italy Rainfall, groundwater Inclinometer Linear regression Yang et al [106] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS LSTM Miao et al [107] Baishuihe, China Rainfall, reservoir level GNSS, inclinometer GA-SVR, GS-SVR, PSO-SVR Li et al [37] Baishuihe, China Rainfall, reservoir level GNSS LASSO-ELM, Copula (ELM, SVM, RF, kNN) Logar et al [108] Ventor, United Kingdom Rainfall Crackmeter ANN Krkač et al [33] Kostanjek, Croatia Groundwater (change), season GNSS RF Zhou et al [109] Bazimen, China Rainfall, reservoir level GNSS PSO-SVM (GA-SVM, GS-SVM, BPNN) Cao et al [110] Baijiabao, China Rainfall, groundwater, reservoir level GNSS ELM (SVM) Lian et al [111] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS LSSVM, ELM, combination Chen and Zeng [112] Baishuihe, China None GNSS BPNN Du et al [31] Baishuihe & Bazimen, China Rainfall, reservoir level GNSS, inclinometer BPNN Lian et al [113] Buishuihe, China None GNSS EEMD-ELM, M-EEMD-ELM (ANN, BPNN, RBFNN, SVR, ELM) Corominas et al [114] Vallcebre, Spain Groundwater Extensometers Physics Neaupane and Achet [115] Okharpauwa, Nepal…”

Section: Methods (Reference Methods)mentioning

confidence: 99%

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

Natijne

Lindenbergh

Bogaard

2020

Sensors

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Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.

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“…Cao et al [14] had described in detail the deformation evolution of the Baijiabao landslide from June 2007 to July 2012, which showed that the Baijiabao landslide was apparently deforming in its entirety. In October 2015, we revisited this landslide and recorded its current deformation.…”

Section: Landslide Deformationmentioning

confidence: 99%

“…Using the model test method, Zhao et al simulated the deformation of the landslide, respectively, under the conditions of rainfall and reservoir water level variation [13]. Cao et al [14] used an extreme learning machine to predict the displacement of step-like landslides in relation to rainfall, reservoir water level, and groundwater level. Wu et al [15] classified the monthly displacement of the Baijiabao landslide into three deformation phases using two-step cluster analysis: initial, constant, and rapid deformation phases.…”

Section: Introductionmentioning

confidence: 99%

The Coupling Effect of Rainfall and Reservoir Water Level Decline on the Baijiabao Landslide in the Three Gorges Reservoir Area, China

Zhao

et al. 2017

Geofluids

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Rainfall and reservoir level fluctuation are two of the main factors contributing to reservoir landslides. However, in China's Three Gorges Reservoir Area, when the reservoir water level fluctuates significantly, it comes at a time of abundant rainfall, which makes it difficult to distinguish which factor dominates the deformation of the landslide. This study focuses on how rainfall and reservoir water level decline affect the seepage and displacement field of Baijiabao landslide spatially and temporally during drawdown of reservoir water level in the Three Gorges Reservoir Area, thus exploring its movement mechanism. The monitoring data of the landslide in the past 10 years were analyzed, and the correlation between rainfall, reservoir water level decline, and landslide displacement was clarified. By the numerical simulation method, the deformation evolution mechanism of this landslide during drawdown of reservoir water level was revealed, respectively, under three conditions, namely, rainfall, reservoir water level decline, and coupling of the above two conditions. The results showed that the deformation of the Baijiabao landslide was the coupling effect of rainfall and reservoir water level decline, while the latter effect is more pronounced.

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Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors

Cited by 125 publications

References 21 publications

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting

The Coupling Effect of Rainfall and Reservoir Water Level Decline on the Baijiabao Landslide in the Three Gorges Reservoir Area, China

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