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Accurate landslide displacement prediction has great practical significance for mitigating geohazards. Traditional deterministic forecasting methods can provide only a single point value and cannot give the degree of uncertainty associated with the forecast, thereby failing to provide information on predictive confidence. This study applied interval prediction for landslide displacement. Taking the Tanjiahe landslide of the Three Gorges Reservoir Area as an example and considering the impact of seasonal variations in reservoir level and rainfall, the uncertainties associated with landslide displacement prediction were quantified into prediction intervals (PIs) by a bootstrapped least-square support vector machine (LSSVM) method (B-LSSVM). The proposed method consists of three steps: First, the LSSVM and bootstrapping were combined to estimate the true regression means of landslide displacement and the variance with respect to model misspecification uncertainties. Second, a new LSSVM model optimized by a genetic algorithm (GA) was implemented to estimate the noise variance. Finally, the point prediction was derived from the regression means, and the PIs were constructed by combining the regression mean, the model variance, and the noise variance. We applied the proposed method to predict the displacement of four GPS monitoring points of the Tanjiahe landslide, and we comprehensively compared the prediction accuracy and the quality of the constructed PIs with benchmark methods. A simulation and performance comparison showed that the proposed method is a promising technique for providing accurate and reliable prediction results for landslide displacement. [ABSTRACT FROM AUTHOR]

Huangtupo landslide, volumetrically the largest, most complex landslide in the Three Gorges Reservoir region of China, is a dangerous mass on which the district of Badong has been inadvertently situated. Risk remediation efforts include the construction of a large observational tunnel and monitoring system that are unique in the world. This tunnel and its side branches permit detailed mapping of its 3D structure while providing samples for laboratory analysis. The new investigations validate that the Huangtupo landslide is a composite of several independent landslides and that movement occurs along the major rupture zones as well as on interlayer sliding zones in the underlying Badong Formation. Uranium-thorium disequilibrium dating establishes that the northern part of the landslide, called the Riverside Slump, underwent at least two periods of movement at about 100 and 40 ka (ka stands for a thousand years). These events were induced by the steep slope created by the downcutting of the Yangzte River. The results from in situ displacement monitoring over a 7-year period confirm that the central part of the landslide is creeping at a slow, relatively stable rate of about 15 mm/year rather than being in a stage of acceleration under the protection of anchored concrete beams and other defense structures at its toe. Available data suggest that engineering measures can control the independent landslides that together constitute the huge Huangtupo mass, which will avoid the need for costly relocation of thousands of people. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The impoundment of the 660-km long reservoir behind the huge Three Gorges Dam, the world's largest hydropower station, increased regional seismicity and reactivated severe geohazards. Before the reservoir filling was initiated in 2003, the region had approximately two earthquakes per year with magnitudes between 3.0 and 4.9; after the full impoundment in 2008, approximately 14 earthquakes per year occurred with magnitudes between 3.0 and 5.4. In addition, hundreds of landslides were reactivated and are now in a state of intermittent creep. Many landslides exhibit step-like annual pattern of displacement in response to quasi-regular variations in seasonal rainfall and reservoir level. Additional problems include rock avalanches, impulse waves and debris flows. The seriousness of these events motivated numerous studies that resulted in 1) Better insight into the behavior and evolution mechanism of geohazards in the Three Gorges Reservoir Area (TGRA); 2) Implementation of monitoring and early-warning systems of geohazards; and 3) Design and construction of preventive countermeasures including lattice anchors, stabilizing piles, rock bolts, drainage canals and tunnels, and huge revetments. This paper reviews the hydro-geologic setting of TGRA geohazards, examines their occurrence and evolution in the past few decades, offers insight learned from extensive research on TGRA geohazards, and suggests topics for future research to address the remaining challenges. [ABSTRACT FROM AUTHOR]

The Three Gorges Reservoir area, one of the most landslide-prone areas in China, is characterized by widely distributed deep-seated landslides exhibiting creep deformation due to rainfall and reservoir fluctuation. Thresholds, which are a key component for a reliable landslide early warning system, are still lacking for the prediction of movements of deep-seated reservoir landslides with creep deformation because information about reservoir fluctuation indicators is lacking, uncertainty is ignored, and binary output is provided. The risk threshold, defined as the tolerance criteria for risks that will lead to action, is an effective measure of the degree of uncertainty. In the present study, a hybrid approach utilizing kernel density estimation, a copula function, and the value at risk is proposed for the estimation of the risk threshold for the Baishuihe Landslide, a typical deep-seated landslide in the Three Gorges Reservoir area. Historical observations over approximately 15 years including rainfall, reservoir fluctuation, and landslide velocity were used to extract the risk threshold. A three-level risk threshold describing the minimum magnitudes of rainfall and reservoir fluctuation for changing the landslide movement state under three confidence levels was developed. A three-level risk response procedure, including risk responses in yellow alert, orange alert, and red alert, is proposed for risk management. Given its successful application, the present approach can be used to estimate the risk threshold for deep-seated landslides. [ABSTRACT FROM AUTHOR]

Hydraulic parameters are key data for calculating groundwater level, which is critical for assessing reservoir landslide stability. However, the quantitative understanding the influence of hydraulic parameters on the groundwater level of a reservoir landslide remains limited. In this paper, we apply a novel global sensitivity analysis method, PAWN, to quantify the sensitivity of hydraulic properties. The Shuping landslide, which is a typical reservoir colluvial landslide located in the Three Gorges Reservoir area, China, is used as a study case. The hydraulic parameters are first sampled within their entire feasibility space by the Latin hypercube sampling method. These samples are then used as inputs into a nonintrusive finite element program to automatically compute the corresponding groundwater level outputs. Finally, sensitivity indices are calculated based on the input–output dataset via the PAWN method. The global sensitivity analysis results provide useful guidelines for site investigation and reservoir colluvial landslide model simplification and calibration. [ABSTRACT FROM AUTHOR]

Reservoir landslide evolution is a complex multi-fields dynamic process which involves the interaction of the seepage field, stress field, displacement field and temperature field. After installing the stabilizing structure into landslide, the landslide and stabilizing structure will establish the landslide-stabilization system and lead to a more complex multi-fields evolution process under the reservoir operation. To study this complex problem, this paper designs and establishes a comprehensive in-situ multi-fields information monitoring platform which has been installed on Majiagou landslide in Three Gorges Reservoir area. The multi-fields information monitoring platform has four components: the landslide-stabilization system, the in-situ multi-field monitoring system, the data transmission and receiving system, and the database management system. The landslide-stabilization system includes Majiagou landslide and two experimental reinforced concrete piles. The in-situ monitoring system consists of landslide surface deformation monitoring system, experimental piles monitoring system and boreholes monitoring system. The landslide surface deformation monitoring system includes GPS stations and fiber lines. The experimental pile monitoring system consists of earth pressure cells around piles, stress and strain gauges on steel bars, and strain and temperature fiber sensors in piles. The borehole monitoring system includes boreholes with inclinometers, water pressure and water level gauges, fiber sensors. Therefore, the in-situ monitoring system can monitor the seepage, stress, displacement and temperature fields in the landslide, as well as the force and deformation in the experimental piles. Currently, the data transmission and management system is under construction and will be finished soon. Current monitoring results show that the landslide moves along three slip surfaces and the deformation of the experimental piles is influenced by reservoir operation. This platform can provide multi-fields data for scholars to study the evolution of landslide-stabilization system. [ABSTRACT FROM AUTHOR]

Monitoring data show that many landslides in the Three Gorges region, China, undergo step-like displacements in response to the managed, quasi-sinusoidal annual variations in reservoir level. This behavior is consistent with motion initiating when the reservoir water level falls below a critical level that is intrinsic to each landslide, with the subsequent displacement rate of the landslide being proportional to the water depth below that critical level. Most motion terminates when the water level rises back above the critical level, so the annual step size is the time integral of the instantaneous displacement rate. These responses are incorporated into a differential equation that is easily calibrated with monitoring data, allowing prediction of landslide movement from actual or anticipated reservoir level changes. Model successes include (1) initiation and termination of the annual sliding steps at the critical reservoir level, producing a series of steps; (2) prediction of variable step size, year to year; and (3) approximate prediction of the shape and size of each annual step. Annual rainfall correlates poorly with step size, probably because its effect on groundwater levels is dwarfed by the 30 m annual variations in the level of the Three Gorges Reservoir. Viscous landslide behavior is suggested. [ABSTRACT FROM AUTHOR]

Abstract Geological disasters have occurred frequently in the Three Gorges Reservoir area. Therefore, the stability and failure mode research on riverside landslides has become increasingly important. Taking the toe of Liangshuijing landslide as a case study and combining the geological conditions of the Three Gorges Reservoir area, a centrifuge slope model is constructed. In addition, the fluctuation of the water level is simulated in the centrifuge model test. Based on the digital photography, laser displacement sensors (LDS), pore pressure transducers (PPT) and earth pressure sensors (E), multi-physical data are obtained. Finally, numerical simulation is carried out to analyze the stability and deformation characteristics of the slope model. The results indicate that: (1) At the water level rise stage, the model is dominated by the vertical displacement. And the horizontal displacement is much larger than the vertical displacement at the drainage stage. (2) Cracks first appear at the toe of the slope and extend to the middle and rear of the model gradually. Then, deformation occurs in the middle platform of the slope. (3) At the water level rise stage, the factor of safety increases rapidly. When the reservoir water level remains stable, the stability of the slope decreases slowly. And at the drainage stage, the factor of safety decreases first and then increases. (4) There is a significant positive linear correlation between the decline rate of the safety factor and the drainage rate over a certain test range. And the stability of the landslides on the reservoir banks would become worse gradually during the long-term cyclic operation of the Three Gorges Reservoir. Highlights • The fluctuation of the water level is simulated in the centrifuge model test. • Qualitative and quantitative method are combined to analysis the evolution of the landslide. • The failure mode and the deformation mechanism of the slope are revealed by the centrifuge model test. [ABSTRACT FROM AUTHOR]

Landslide early warning systems can be implemented based on the monitoring and prediction of landslide displacements. The internal mechanisms of landslides are very complex, and precise mechanistic models of landslides are difficult to obtain; therefore, data-driven models are usually applied. From the perspective of dynamic system theory, landslide development should be considered a dynamic process. Because traditional models, such as feed-forward neural networks, can only express static relationships, the applicability of these static models is quite limited in landslide prediction tasks. In our study, recurrent neural networks are established and trained into dynamic predictors of landslide displacement using a training algorithm named reservoir computing. A shortcoming of the reservoir computing algorithm is that the models will not perform well with insufficiently large training sets. Because practical landslide displacement recordings are usually quite short, interpolation techniques are employed to enhance the training of the dynamic predictors. The method proposed in this paper is applied to predict the developments of three landslides in the Three Gorges area, and the predicted values are close to the actual measurements. Improvements relative to sophisticated static predictors and basic dynamic predictors are obvious. [ABSTRACT FROM AUTHOR]

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD-ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD-ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China. [ABSTRACT FROM AUTHOR]

The construction of the huge Three Gorges reservoir affected a large region, and the resultant geological and environmental impacts have caused global concern. The remarkable, 30-m annual fluctuation in the reservoir water level poses a significant threat to slope stability in this area. Four hundred sixty-two landslides were identified in the Zigui basin using historical records, satellite images, field investigations and unmanned aerial vehicle (UAV) observations, enabling the construction of a complete landslide database and distribution map. Three failure modes of landslides in the Zigui basin are used to illustrate the major factors that govern reservoir-induced landslides. The results show that (1) > 99% of identified landslides occur on slopes angle <47°, while >80% occur at elevations below 600 m; (2) Jurassic Niejiashan Formation is highly prone to landslides; (3) Low reservoir levels of 145 m to 155 m greatly reduce slope stability. Based on the information entropy method that use the conditional probability of different influencing factors and principal component analysis as inputs, the relative contributions of various influencing factors are quantified and a landslide susceptibility map was drawn. This susceptibility map helps define the countermeasures that will best reduce fatalities and property losses for areas having different landslide conditions and susceptibilities. • Database of 462 reservoir-induced landslides in Zigui basin, TGR was created. • Identifying failure modes and quantifying contributions of influential factors. • Conditional probability and information entropy model yields landslide susceptibility. • Susceptibility map, failure modes, and strategies suppressing the slope instability. [ABSTRACT FROM AUTHOR]