Search Results (7)

Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature selection. This study investigates the impact of various feature selection techniques on the performance of ML algorithms for landslide displacement prediction. The Shuping and Baishuihe landslides in China’s Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent ML algorithms. Both static ML models, including backpropagation neural network (BPNN), support vector machine (SVM), and dynamic models, such as long short-term memory (LSTM), and gated recurrent unit (GRU), are included. Each ML model is evaluated under three feature engineering techniques: raw multivariate time series, and feature selection under maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). The results demonstrate that appropriate feature selection methods could significantly improve the performance of static ML models. In contrast, dynamic models effectively leverage inherent capabilities in capturing temporal dynamics within raw multivariate time series, seeing marginal gains with extensive feature engineering compared to no feature selection strategy. The optimal feature selection approach varies based on the ML model and specific landslide, highlighting the importance of case-specific assessments. The findings in this study offer guidance on integrating feature selection techniques with different machine learning models to maximize the robustness and generalizability of data-driven landslide displacement prediction frameworks. Full article

Landslide displacement prediction is of great significance for the prevention and early warning of slope hazards. In order to enhance the extraction of landslide historical monitoring signals, a landslide displacement prediction method is proposed based on the decomposition of monitoring data before prediction. Firstly, based on the idea of temporal addition, the sparrow search algorithm (SSA) coupled with the variational modal decomposition (VMD) algorithm is used to decompose the total landslide displacement into trend item, periodic item and random item; then, the displacement values of the subitems are fitted by using the long and short-term memory (LSTM) neural network, and the predicted cumulative landslide displacement is obtained by adding up the predicted values of the three subsequences. Finally, the historical measured data of the Shuping landslide is taken as an example. Considering the effects of seasonal rainfall and reservoir water level rise and fall, the displacement of this landslide is predicted, and the prediction results of other traditional models are compared. The results show that the landslide displacement prediction model of SSA-VMD coupled with LSTM can predict landslide displacement more accurately and capture the characteristics of historical signals, which can be used as a reference for landslide displacement prediction. Full article

Landslides are caused by rainfall as one of the main factors. In order to study the effect of rainfall on the physical and mechanical parameters of landslides, a physical model of the colluvium landslide is created in laboratory conditions with silty clay, river sand, and gravel, taking Shuping landslide in the Three Gorges Reservoir area as the prototype. The artificial rainfall is applied to the accumulation model, which is steady for 60 h, and then the gravel soil is taken out along the different elevations of the colluvium for the permeability test and direct shear test, and the evolution law of changes in porosity, the permeability coefficient, and the shear strength parameters along the elevation are studied. Combined with XRF and NMR tests, the spatial variation of the permeability coefficient and shear strength parameters is discussed from the perspective of chemical elements, minerals content, and porosity, and the stability analysis of a colluvium landslide is carried out considering the influence of parameters along the elevation. The results show that under the action of rainfall seepage, the fine particles of clay are transported from upslope to downslope, resulting in more and more fine particles of clay at the toe slope. The original pores are gradually filled, the cementation between particles is stronger, the corresponding cohesion is increased, and the permeability coefficient is reduced. Due to the loss of fine particles at the upslope, the relative content of coarse particles increases, leading to an increase in the internal friction angle. The variability of the slope’s physical and mechanical parameters is a result of the spatial transport of clay particles in the colluvium caused by the rainfall seepage above. Specifically, the permeability coefficient and internal friction angle from upslope to downslope decrease linearly under the action of rainfall, but the law of the cohesion increases linearly. The upslope’s permeability coefficient and internal friction angle decrease by 11% and 8% compared to those of the downslope, while the cohesion increases by 168%. The results of FLAC^3D numerical calculation of Shuping landslide show that the maximum deformation in the X direction of 145 m and 175 m water level increases by 12% and 42%, and the safety factor decreases by 0.63% and 5% under the combined action of rainfall and the reservoir water level, that is, when considering the variation of parameters along the elevation of the landslide. The research findings provide a better understanding of the spatial parameters in similar colluvium bodies under rainfall action. Full article

Predicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). The least squares support vector machine (LSSVM) model optimized by particle swarm optimization (PSO) is selected to predict the landslide displacement prediction based on rainfall and reservoir water level (RWL). Five parameters, including rainfall over the previous month, rainfall over the previous two months, RWL, change in RWL over the previous month and period displacement over the previous half year, are selected as the input variables. The relationships between the five parameters and the landslide displacement are revealed by grey correlation analysis. The PSO-LSSVM model is used to predict the periodic term displacement (PTD), and the least squares method is applied to predict the trend term displacement (TTD). With the same input variables, the back propagation (BP) model and the PSO-SVM model are also developed for comparative analysis. In the PSO-LSSVM model, the R² of three monitoring stations is larger than 0.98, and the MAE values and the RMSE values are the smallest among the three models. The outcomes demonstrate that the PSO-LSSVM model has a high accuracy in predicting landslide displacement. Full article

In the process of rapid drawdown of reservoir water level, the seepage force in the slide mass is an important factor for the stability reduction and deformation increment of many landslides in the reservoir areas. It is feasible to improve the stability of seepage-induced landslide by employing a drainage well to reduce or eliminate the water head difference that generates the seepage force. In this paper, the Shuping landslide, a typical seepage-induced landslide in the Three Gorges Reservoir area of China, is taken as an example. A series of numerical simulations were carried out to figure out the seepage field, and the Morgenstein–Price method was adopted to calculate the landslide stability. Then the influence of horizontal location of the drainage well, drainage well depth, drainage mode on the landslide treatment effect, and the applicability of drainage well were analyzed. The results show that: (1) landslide stability increases obviously with the well depth in the slide mass, while the increment of landslide stability with the well depth is limited in the slide bed; (2) the sensitivity of the stability improvement with the depth is greater than that with the horizontal positions of the drainage wells in the slide mass; (3) the drainage well is suggested to be operated when the reservoir water falls rather than operates all the time; and (4) the drainage method is most suitable for landslides with low and medium permeability. These results provide deep insights into the treatment of seepage-induced landslides. Full article

Landslides constitute a major threat to people’s lives and property in mountainous regions such, as in the Three Gorges area in China. Synthetic Aperture Radar Interferometry (InSAR) with its wide coverage and unprecedented displacement measuring capabilities has been widely used in landslide monitoring. However, it is difficult to apply traditional InSAR techniques to investigate landslides having large deformation gradients or moving primarily in north-south direction. In this study, we propose a time series split-bandwidth interferometry (SBI) procedure to measure two dimensional (azimuth and range) displacements of the Shuping landslide in the Three Gorges area with 36 TerraSAR-X high resolution spotlight (HS) images acquired from February 2009 to April 2010. Since the phase based SBI procedure is sensitive to noise, we focused on extracting displacements of corner reflectors (CRs) installed on or surrounding the Shuping landslide. Our results agreed well with measurements obtained by the point-like targets offset tracking (PTOT) technique and in-situ GPS stations. Centimeter level accuracy could be achieved with SBI on CRs which shows great potential in futures studies on fast moving geohazards. Full article

Time-series SAR/InSAR techniques have proven to be effective tools for measuring landslide movements over large regions. Prior studies of these techniques, however, have focused primarily on technical innovation and applications, leaving coupling analysis of slope displacements and trigging factors as an unexplored area of research. Linking potential landslide inducing factors such as hydrology to SAR/InSAR derived displacements is of crucial importance for understanding landslide deformation mechanisms and could support the development of early-warning systems for disaster mitigation and management. In this study, a sequential data assimilation method named the Ensemble Kalman filter (EnKF), is adopted to explore the response mechanisms of the Shuping landslide movement in relation to hydrological factors. Previous research on the Shuping landslide area shows that the reservoir water level and rainfall are the two main triggering factors in slope failures. To extract the time-series deformations for the Shuping landslide area, Pixel Offset Tracking (POT) technique with corner reflectors was adopted to process the TerraSAR-X StripMap (SM) and High-resolution Spotlight (HS) images. Considering that these triggering factors are the primary causes of displacement fluctuations in periodic displacement, time-series decomposition was carried out to extract the periodic displacement from the POT measurements. The correlations between the periodic displacement and the inducing factors were qualitatively estimated through a grey relational analysis. Based on this analysis, the EnKF method was adopted to explore the response relationships between the displacements and triggering factors. Preliminary results demonstrate the effectiveness of EnKF in studying deformation response mechanisms and understanding landslide development processes. Full article