Application of GWO-ELM Model to Prediction of Caojiatuo Landslide Displacement in the Three Gorge Reservoir Area

Abstract

<jats:p>In order to establish an effective early warning system for landslide disasters, accurate landslide displacement prediction is the core. In this paper, a typical step-wise-characterized landslide (Caojiatuo landslide) in the Three Gorges Reservoir (TGR) area is selected, and a displacement prediction model of Extreme Learning Machine with Gray Wolf Optimization (GWO-ELM model) is proposed. By analyzing the monitoring data of landslide displacement, the time series of landslide displacement is decomposed into trend displacement and periodic displacement by using the moving average method. First, the trend displacement is fitted by the cubic polynomial with a robust weighted least square method. Then, combining with the internal evolution rule and the external influencing factors, it is concluded that the main external trigger factors of the periodic displacement are the changes of precipitation and water level in the reservoir area. Gray relational degree (GRG) analysis method is used to screen out the main influencing factors of landslide periodic displacement. With these factors as input items, the GWO-ELM model is used to predict the periodic displacement of the landslide. The outcomes are compared with the nonoptimized ELM model. The results show that, combined with the advantages of the GWO algorithm, such as few adjusting parameters and strong global search ability, the GWO-ELM model can effectively learn the change characteristics of data and has a better and relatively stable prediction accuracy.</jats:p>

Hysteretic Behavior of Geopolymer Concrete with Active Confinement Subjected to Monotonic and Cyclic Axial Compression: An Experimental Study

“…For the triaxial compression tests, as shown in ( Figure 2 b), two external LVDTs and one circumferential displacement gauge were still used to measure the axial and circumferential deformation. Before the concrete sample was inserted into the rubber sleeve of the Hoek cell, a thin layer of lubricating wax was applied on the surface of the circumferential displacement gauge and the steel conducting wire to reduce the friction between the gauge and the rubber sleeve [ 21 , 22 , 23 , 24 , 25 ]. In this study, two load schemes were used and the lateral pressure remained constant during the triaxial compression test at designed values of 6 MPa, 12 MPa, 18 MPa, or 24 MPa.…”

Settlement Prediction of Foundation Pit Excavation Based on the GWO‐ELM Model considering Different States of Influence

“…Extreme learning machine (ELM) is a new algorithm proposed in recent years [19,20]; it is based on single hidden layer feedforward neural networks (SLFNs) and solved the problem that the number of hidden layers in the neural network is difficult to determine. Recent research regarding the application of ELM in geotechnical engineering is sufficiently applied, such as predicting compressive strength of lightweight foamed concrete using extreme learning machine model [21], prediction of shield tunneling-induced ground settlement using machine learning techniques [22], and landslide displacement prediction based on extreme learning machine [23]. Compared with traditional prediction models such as BP neural network and SVM, ELM learns faster, has good generalization ability, and produces the unique optimal solution.…”

Failure Mechanism of Colluvial Landslide Influenced by the Water Level Change in the Three Gorges Reservoir Area

“…Lots of landslides have been reported in the reservoir area, with the increased number of the large-scale hydraulic projects [1][2][3][4][5][6][7]. In addition, the fluctuation of reservoir water level has an important impact on the landslide stability [7][8][9][10].…”

“…The Three Gorges Reservoir area (TGRA) is significantly affected by catastrophic landslides, and the colluvial landslides often occur in this area due to reservoir water level changes [11]. Since June 2003 when the Three Gorges Pro-ject was completed, around 2619 landslides have failed due to the fluctuation of the reservoir water level, and 670 mountains are under unstable status [5][6][7][12][13][14][15][16][17][18][19][20]. Therefore, it is important to study the failure mechanism to provide guideline for the risk assessment of the landslides.…”