Reservoir Landslide Research Articles

Novel approach to quantitative risk assessment of reservoir landslides using a hybrid CNN-LSTM model

Novel approach to quantitative risk assessment of reservoir landslides using a hybrid CNN-LSTM model

Prediction of impulse waves generated by the potential failure of large deposits in the Rumei Reservoir, Lancang River, China

The impulse wave generated by the reservoir landslide seriously endangers the surrounding safety. In this paper, the failure mode of a large deposit in Rumei Reservoir, China was first explored by centrifuge test. Thereafter, a physical model based on Froude similarity (scale 1:200) and a numerical model based on fluid‒solid coupling were constructed. The failure motion process and the generated impulse waves of the deposits at high (2895 m) and low (2750 m) water levels are studied in detail. The results of the two models are similar. The velocity and wave height of the deposits at the 2750 m water level are greater than those at the 2895 m water level. The maximum wave heights generated in the river under both conditions exceed 46 m and 22 m, respectively, and the maximum run-ups formed on the opposite bank are 71 m and 30 m, respectively. This also suggests that landslides in the near field area are dangerous. Waves at both water levels will not overtop the dam, but attention should be given to the effect of the first wave at the 2895 m water level on the dynamic water pressure in the key area of the dam.

Experimental and numerical study of shear strength on the slide zone soil by the coupling of seepage and shear test

The stability of reservoir landslides is determined by the strength of the slide zone. Using both experimental and numerical techniques to analyze the strength of the slide zone has been widely used in the research of reservoir landslides. However, few models consider the strength of the coupling between seepage and shear. This paper aims to analyze the shear strength of slide zone soil combined with different stresses by using the directed shear test and particle flow code PFC2D. The results indicated that slide zone soil clearly experienced the strain-hardening without seepage. While a specific strain-softening phenomenon was observed because the introduction of seepage reduced the shear strength significantly, the internal friction angle demonstrated a higher sensitivity to seepage stress compared to cohesion as the seepage stress increased. The direction and amount of flow velocity dynamically alter during the shear process, as revealed by numerical simulation results, suggesting that the shear strength was nonlinear with increasing seepage stress and changed structure. It can be assumed that seepage deterioration in the Huangtupo landslide is a dynamic process that influences the structure, strength and permeability characteristics.

Numerical simulation of deposit-landslide instability induced by water level decrease: a case study of RS deposit in Lancang River

Changes in reservoir water level often trigger landslides along the reservoir banks, which are common geological hazards that significantly affect the construction and operation of hydropower projects. In this paper, the stability of the RS reservoir landslide in the southwestern region of China is investigated. A discrete element seepage analysis model is constructed based on the theory of changes in the infiltration surface of the deposit caused by water level drawdown. The model is then embedded into a continuous-discontinuous three-dimensional numerical simulation method to systematically investigate the influence of reservoir water level decrease on the stability of the RS deposit. The results indicate that, under the condition of a sudden drop in reservoir water level decrease, the deposit experiences destabilization failure. Its sliding velocities are mainly influenced by seepage forces and gravity, while sliding displacements are influenced by seepage forces and topographic conditions. The landslide process shows distinct stratification characteristics. The portion of the deposit that slides into the river channel is mainly distributed between an altitude of 2655 m and 2870 m. The research results provide a scientific basis for studying the mechanism of slope failure and landslide disaster chain under the condition of reservoir water level lowering.

Probabilistic back analysis of reservoir landslide considering hydro-mechanical coupled observations

Precisely assessing statistical parameters to characterize spatial soil variability presents a significant challenge in probabilistic slope stability analysis, primarily due to inherent soil uncertainties and limited field-specific data. Probabilistic back analysis, recognized as an effective and reliable technique, offers a rational method for utilizing observational data to invert parameters. Nevertheless, previous investigations into parameter inversion for slope stability analysis have seldom considered the coupling of hydro-mechanical characteristics in reservoir landslides. This study proposes a novel integrated Bayesian framework to perform back analysis of reservoir landslides, incorporating the spatial variability of hydro-mechanical parameters. Within this framework, a hypoplastic constitutive model is developed to characterize the step-like deformation of the reservoir slope. The posterior knowledge of the saturated permeability coefficient and shear strength parameters is obtained by collecting field monitoring data of the underground water level and ground displacement. The Maliulin landslide, located in the Three Gorges Reservoir area of China, is used as an illustrative example to validate the proposed framework through back analysis of spatially variable soil parameters and probabilistic stability analysis. The results demonstrate that the proposed Bayesian updating framework significantly reduces the uncertainties associated with statistical values of soil parameters, providing more accurate and reasonable updated soil parameters for probabilistic slope stability analysis.

System Reliability Analysis of Reservoir Landslides: Insights from Long-Term Reservoir Operation

System Reliability Analysis of Reservoir Landslides: Insights from Long-Term Reservoir Operation

Advancing reservoir landslide stability assessment via TS-InSAR and airborne LiDAR observations in the Daping landslide group, Three Gorges Reservoir Area, China

Advancing reservoir landslide stability assessment via TS-InSAR and airborne LiDAR observations in the Daping landslide group, Three Gorges Reservoir Area, China

Deformation mechanism-assisted deep learning architecture for predicting step-like displacement of reservoir landslide

Reservoir landslides in the Three Gorges Reservoir, China, exhibit prolonged slow motion and the potential for catastrophic events due to fluctuations in reservoir levels and intense rainfall episodes. Their distinct step-like deformation characteristics, involving rapid transformation processes of different states, pose challenges for accurate early warning and prediction. Previous forecasting models have often struggled with limited accuracy. This study introduces a mechanism-assisted deep learning model, leveraging the Informer architecture, to predict prolonged step-like reservoir landslide displacement. Utilizing a 15-year continuous monitoring dataset of the Baishuihe landslide, this model investigates the landslide mechanism, identifies influencing conditions underlying the step-wise behavior, and customizes input features for the prediction model by integrating optimized variational mode decomposition and wavelet analysis. Additionally, the dynamic correlation and hysteresis analysis between triggering factors and displacement offer valuable physical insights into the model and enhance the interpretability of the model. The model is further tailored to accommodate features of the monitoring dataset associated with landslide evolution by integrating a global multi-head attention mechanism and pooling layers, enabling the capture of both globe dependencies and local critical features of the model inputs. Through rigorous model training, performance evaluation, and tuning, the proposed model efficiently predicts step-wise landslide displacement, particularly during short-term rapid transitions between creep-mutation states.

Multi-scale investigation on staged deterioration mechanism of sliding-zone soils induced by reservoir fluctuations

Water level fluctuations in the reservoir deteriorate soils and rocks on the bank landslides by drying-wetting (D-W) cycles, which results in a significant decrease in mechanical properties. A comprehensive understanding of deterioration mechanism of sliding-zone soils is of great significance for interpreting the deformation behavior of landslides. However, quantitative investigation on the deterioration characteristics of soils considering the structural evolution under D-W cycles is still limited. Here, we carry out a series of laboratory tests to characterize the multi-scale deterioration of sliding-zone soils and reveal the mechanism of shear strength decay under D-W cycles. Firstly, we describe the micropores into five grades by scanning electron microscope and observe a critical change in porosity after the first three cycles. We categorize the mesoscale cracks into five classes using digital photography and observe a stepwise increase in crack area ratio. Secondly, we propose a shear strength decay model based on fractal theory which is verified by the results of consolidated undrained triaxial tests. Cohesion and friction angle of sliding-zone soils are found to show different decay patterns resulting from the staged evolution of structure. Then, structural deterioration processes including cementation destruction, pores expansion, aggregations decomposition, and clusters assembly are considered to occur to decay the shear strength differently. Finally, a three-stage deterioration mechanism associated with four structural deterioration processes is revealed, which helps to better interpret the intrinsic mechanism of shear strength decay. These findings provide the theoretical basis for the further accurate evaluation of reservoir landslides stability under water level fluctuations.

Maximum initial primary wave model for low-Froude-number reservoir landslides based on wave theory

Maximum initial primary wave model for low-Froude-number reservoir landslides based on wave theory

Research on Reservoir Landslide Thrust Based on Improved Morgenstern-Price Method

Research on Reservoir Landslide Thrust Based on Improved Morgenstern-Price Method

Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction

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.

Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

Step-like displacement prediction of reservoir landslides based on a metaheuristic-optimized KELM: a comparative study

Inversion Study on Landslide Seepage Field Based on Swarm Intelligence Optimization Least-Square Support Vector Machine Algorithm

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage.

A comparative evaluation of clustering methods and data sampling techniques in the prediction of reservoir landslide deformation state

ABSTRACT Landslides exhibiting step-wise deformation characteristics are extensively dispersed throughout the Three Gorges Reservoir (TGR) region of China. Predicting the deformation state of landslides in TGR holds paramount significance in landslide early warning and risk management. Machine learning-based landslide deformation state prediction is a combination of clustering and imbalanced classification. This paper compares the efficacy of three prevalent clustering methods, namely K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Model (GMM), in the clustering analysis process. Furthermore, the paper evaluates the performance of three widely-used data sampling technologies, namely Synthetic Minority Oversampling Technique (SMOTE), SMOTE-Edited Nearest Neighbors (SMOTE-ENN), and ADAptive SYNthetic Sampling (ADASYN), in the imbalanced classification process. The Baijiabao and Bazimen landslides in the TGR region, which exhibit step-wise deformation characteristics, are used as case studies. Results indicate that DBSCAN and GMM exhibit significant advantages in the clustering process. Meanwhile, the mixture models that integrate oversampling technologies and classification algorithms perform exceptionally well in imbalanced classification. The aforementioned algorithms are recommended for predicting the deformation states of step-wise landslides in the TGR region. The machine learning-based predictive models can serve as potent instruments in facilitating the implementation of early warning systems aimed at mitigating landslide risks.

Coupling effect of impoundment and irrigation on landslide movement in Maoergai Reservoir area revealed by multi-platform InSAR observations

The mountainous areas of Southwest China are crucial regions for hydropower resource development. The unique geological environment and numerous constructed hydropower stations make landslides the primary geological hazard for the region. Therefore, the deformation monitoring and mechanism analysis of reservoir landslides have attracted extensive attention from the academic community. The Maoergai Reservoir (MEGR) is located in the middle reaches of the Heishui River in Sichuan Province, China. Following impoundment, numerous active slopes have arisen in the reservoir area, with the Xierguazi-Mawo landslide complex (XMLC) potentially being the most threatening. In this paper, we first acquired the surface displacement of the MEGR area, before and after impoundment, using time series Synthetic Aperture Radar Interferometry (InSAR) analysis of the ALOS-1/PALSAR-1, ALOS-2/PALSAR-2 and Sentinel-1 datasets. Then, the three-dimensional (3D) deformation field was inverted by combining multi-track InSAR observations and a topography-constrained model. Results show the presence of 47 active landslides within the 675 km2 reservoir area, a significant increase compared to the seven landslides identified prior to impoundment. Based on the derived 3D velocity vectors, we found that the motion of the XMLC exhibits heterogeneity in terms of spatial and temporal patterns. Further, the displacement time series of all detected active landslides suggests that irrigation and water level fluctuations are two major factors influencing the kinematic behaviors of landslides. Irrigation accelerates landslide movement and thus permanently alters the deformation trend, while water level fluctuations cause seasonal oscillations in the displacement time series with a time lag of approximately 100 days. The above results can contribute to landslide management and prevention in the MEGR area and provide a representation for numerous water reservoirs in southwest China.

Numerical Simulation of Rainfall‐induced Xianchi Reservoir Landslide in Yunyang, Chongqing, China

AbstractA calamitous landslide happened at 22:00 on September 1, 2014 in the Yunyang area of Chongqing City, southwest China, enforcing the evacuation of 508 people and damaging 23 buildings. The landslide volume comprised 1.44 million m3 of material in the source area and 0.4 million m3 of shoveled material. The debris flow runout extended 400 m vertically and 1600 m horizontally. The Xianchi reservoir landslide event has been investigated as follows: (1) samples collected from the main body of landslide were carried out using GCTS ring shear apparatus; (2) the parameters of shear and pore water pressure have been measured; and (3) the post‐failure characteristics of landslide have been analyzed using the numerical simulation method. The excess pore‐water pressure and erosion in the motion path are considered to be the key reasons for the long‐runout motion and the scale‐up of landslides, such as that at Xianchi, were caused by the heavy rainfall. The aim of this paper is to acquired numerical parameters and the basic resistance model, which is beneficial to improve simulation accuracy for hazard assessment for similar to potentially dangerous hillslopes in China and elsewhere.

Revisiting spatiotemporal evolution process and mechanism of a giant reservoir landslide during weather extremes

An updated comprehension of landslide kinematics is a prerequisite for developing effective risk assessment and emergency management in the context of climate variability and anthropogenic disturbances. This study employs a multidisciplinary approach, taking the Outang landslide in China's Three Gorges Reservoir area as a case study. We examined the spatiotemporal surface processes and time- and subzone-dependent hydrometeorological factors from 2007 to 2021, particularly identifying rainfall regimes and reservoir water levels that trigger landslide movements. Given the uneven distribution of GNSS surface markers, we introduced weighted monthly mean displacement (D) and weighted marker frequency (f), enhancing the accuracy of our insights into distinct active subzones of the landslide. Preliminary results revealed July, September and June as months of higher susceptibility, as well as months with low water levels. The early-stage deformation control by reservoir water levels is being increasingly overshadowed by rainfall, due to extreme weather events and localized anti-slip measures. Long-term surface processes have also highlighted the transformation of landslide failure modes towards a compound pattern, from a predominantly retrogressive to a thrust-dominated pattern, and even overall translation coupled with localized rotation. Furthermore, the comparative analysis of normalized displacement/strain demonstrates the power and potential of integrating multi-source monitoring data. By revisiting the evolution processes and potential deformation mechanisms of the Outang landslide, we are getting closer to understanding more about the dynamics of giant landslides in reservoir areas, and illuminate the pathway towards enhanced risk management and emergency response strategies.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically, landslides have been the primary type of geological disaster worldwide. Generally, the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations. Moreover, the stability of reservoir banks changes with the long-term dynamics of external disaster-causing factors. Thus, assessing the time-varying reliability of reservoir landslides remains a challenge. In this paper, a machine learning (ML) based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction. This study systematically investigated the prediction performances of three ML algorithms, i.e. multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Additionally, the effects of the data quantity and data ratio on the predictive power of deep learning models are considered. The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides. The CNN model outperforms both the MLP and LSTM models in predicting the failure probability. Furthermore, selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Model test study on natural frequency evolution and early warning of reservoir landslides under water level fluctuations

Model test study on natural frequency evolution and early warning of reservoir landslides under water level fluctuations