Mechanism of the slow-moving landslides in Jurassic red-strata in the Three Gorges Reservoir, China

Landslides in Jurassic red-strata make up a great part of geohazards in the Three Gorges Reservoir (TGR) in China. Most of them begin to move slowly with the accumulated displacement increasing stepwise, which corresponds to seasonal rainfall and 30 m of reservoir water level fluctuation (145 m to 175 m on elevation). We analyzed the movement of 21 slow moving landslides in the Jurassic red-strata in TGR, and found that all these landslides involved two differing processes; one is the sliding process with different shear speeds of soils within the sliding zone (landslide activity), and the other one is in steady state with different durations (dormant state). This means that the soil within the sliding surface may experience shearing at different shear rates and recovery in shear strength during the dormant period. To clarify the mechanism of this kind of movement, we took soil samples from the sliding surface of Xiangshanlu landslide, which occurred on August 30, 2008 in the Jurassic red-strata in TGR, and examined the shear rate dependency and recovery of shear resistance by means of ring shear tests. The results of tests at different shear rates show that the shear strength is positively dependent on the shear rate, and can be recovered within a short consolidation duration after the shearing ceased. By increasing the pore-water pressure (PWP) from the upper layer of the sample, we also examined the initiation of shearing which can simulate the restart of landsliding due to the fluctuation of groundwater level caused by rainfall or changes in reservoir water level. The monitored PWP near the sliding surface revealed that there was a delayed response of PWP near the sliding surface to the applied one. This kind of delayed response in pore-water pressure may provide help for the prediction of landslide occurrence due to rainfall or fluctuation of reservoir water level

Influencing factor analysis and displacement prediction in reservoir landslides − a case study of Three Gorges Reservoir (China)

Potrebno je učinkovito predviđati kako će se vremenski odvijati kumulativni pomaci povezani s klizištima akumulacija. Međutim, tradicionalne metode ne obuhvaćaju dinamički uspostavljene odnose između deformacije klizišta i faktora koji na to utječu. Dakle, uveden je novi pristup na temelju eksponencijalnog izglađivanja (EI) i multivarijatnih metoda ekstremno učećeg stroja kako bi se otkrili čimbenici od utjecaja na deformacije klizišta i predvidjele vrijednosti pomaka klizišta. Prvo su analizirani faktori koji utječu na deformacije klizišta akumulacije. Zatim je EI postupak rabljen za predviđanje trajanja trenda pomaka i dobivanje trajanja periodičnog pomaka određivanjem trajanja trenda iz kumulativnog pomaka. Dalje, analizirani su multivarijatni utjecajni faktori kako bi objasnili trajanje periodičnog pomaka. Nakon toga, postavljen je model ekstremno učećeg stroja kako bi se predvidjelo trajanje periodičnog pomaka na temelju multivarijatne analize utjecajnih čimbenika. Konačno, dobivene su vrijednosti predviđanja kumulativnog pomaka dodavanjem vrijednosti predviđanja trenda i periodičnog pomaka. Bazimen i Baishuihe klizišta u području Three Gorges Reservoir odabrana su kao studije slučaja. Predloženi model EI-multivarijatnog ekstremno učećeg stroja (ES-MELM) uspoređen je s modelom EI-univarijantnog ekstremno učećeg stroja (ES-ELM). Rezultati pokazuju da je deformacija klizišta akumulacije uglavnom pod utjecajem periodičnih oscilacija razine vode akumulacije i obilnih kiša. Osim toga, predloženi model pridonosi točnijem predviđanju od ES-ELM modela.The developmental tendencies of cumulative displacement time series associated with reservoir landslides influenced by large water reservoirs must be effectively predicted. However, traditional methods do not encompass the dynamic response relationships between landslide deformation and its influencing factors. Therefore, a new approach based on the exponential smoothing (ES) and multivariate extreme learning machine methods was introduced to reveal the influencing factors of landslide deformation and to forecast landslide displacement values. First, the influencing factors of reservoir landslide deformation were analysed. Second, the ES method was used to predict the trend term displacement and obtain the periodic term displacement by determining the trend term from the cumulative displacement. Next, multivariate influencing factors were analysed to explain the periodic term displacement. Then, an extreme learning machine (ELM) model was established to predict the periodic term displacement based on the multivariable analysis of influencing factors. Finally, cumulative displacement prediction values were obtained by adding the trend and periodic displacement prediction values. The Bazimen and Baishuihe landslides in Three Gorges Reservoir Area (TGRA) were selected as case studies. The proposed ES-multivariate ELM (ES-MELM) model was compared to the ES-univariate ELM (ES-ELM) model. The results show that reservoir landslide deformation is mainly influenced by periodic reservoir water level fluctuations and heavy rainfall. Additionally, the proposed model yields more accurate predictions than the ES-ELM model

Time series analysis and long short-term memory neural network to predict landslide displacement

A good prediction of landslide displacement is an essential component for implementing an early warning system. In the Three Gorges Reservoir Area (TGRA), many landslides deform distinctly and in steps from April to September each year under the influence of seasonal rainfall and periodic fluctuation in reservoir water level. The sliding becomes more uniform again from October to April. This landslide deformation pattern leads to accumulated displacement versus time showing a step-wise curve. Most of the existing predictive models express static relationships only. However, the evolution of a landslide is a complex nonlinear dynamic process. This paper proposes a dynamic model to predict landslide displacement, based on time series analysis and long short-term memory (LSTM) neural network. The accumulated displacement was decomposed into a trend term and a periodic term in the time series analysis. A cubic polynomial function was selected to predict the trend displacement. By analyzing the relationships between landslide deformation, rainfall, and reservoir water level, a LSTM model was used to predict the periodic displacement. The LSTM approach was found to properly model the dynamic characteristics of landslides than static models, and make full use of the historical information. The performance of the model was validated with the observations of two step-wise landslides in the TGRA, the Baishuihe landslide and Bazimen landslide. The application of the model to those two landslides demonstrates that the LSTM model provides a good representation of the measured displacements and gives a more reliable prediction of landslide displacement than the static support vector machine (SVM) model. It is concluded that the proposed model can be used to effectively predict the displacement of step-wise landslides in the TGRA.acceptedVersio

Landslide displacement analysis based on fractal theory, in Wanzhou District, Three Gorges Reservoir, China

Slow moving landslide is a major disaster in the Three Gorges Reservoir area. It is difficult to compare the deformation among different parts of this kind of landslide through GPS measurements when the displacement of different monitoring points is similar in values. So far, studies have been seldom carried out to find out the information hidden behind those GPS monitoring data to solve this problem. Therefore, in this study, three landslides were chosen to perform landslide displacement analysis based on fractal theory. The major advantage of this study is that it has not only considered the values of the displacement of those GPS monitoring points, but also considered the moving traces of them. This allows to reveal more information from GPS measurements and to obtain a broader understanding of the deformation history on different parts of a unique landslide, especially for slow moving landslides. The results proved that using the fractal dimension as an indicator is reliable to estimate the deformation of each landslide and to represent landslide deformation on both spatial and temporal scales. The results of this study could make sense to those working on landslide hazard and risk assessment and land use planning

Areview of landslide-generated waves risk and practice of management of hazard chain risk from reservoir landslide

As one of the major types of geological hazards in reservoir areas, the risk analysis of landslides has been a top research topic recently. Landslide-generated waves extend the influence area from the landslide source itself to several kilometers upstream and downstream and greatly expand the type and number of elements and disaster damage. The risk evaluation of landslide generated waves is considered to be a difficult component in the evaluation of landslide risk hazard chains, as involving the intersection of different areas. Firstly, the previous research results in recent decades were synthesized from hazard, vulnerability and risk, the current situation of landslide generated waves risk research and common research methods worldwide were outlined, and the key representative research results were reviewed and analyzed. New progress was introduced, which includes experimental studies considering the complexity of actual river topography, coupled numerical simulation methods focusing on landslide-water interaction mechanisms, and a vulnerability assessment system based on multiple hazard-bearing body types. Secondly, the process and consequences of a number of landslide generated waves risk management cases that have occurred in the Three Gorges Reservoir Area in recent years were described in detail. Finally, according to the author's many years of research experience, new directions and ideas were proposed for the study of landslide-landslide generated waves hazard chain risks, and suggestions were given that surge risk and landslide risk evaluation systems should be merged with each other and developed along the direction of quantification, standardization and refinement

Analysis of Baishuihe landslide influenced by the effects of reservoir water and rainfall

Since the impoundment of the Three Gorges Reservoir in June 2003, a number of new landslides have occurred and existing landslides have been made worse. The 1,260 × 104 m3 Baishuihe landslide, located at 56 km west of the Three Gorges Dam, began to deform more noticeably after the first impoundment in early July 2003. The sliding of the two blocks comprising the landslide, one an active block and the other a relatively stable block, became apparent after approximately 5 years of monitoring. Field recordings show that the landslide displacement is affected by the combined effects of the rainfall and water level in the reservoir. These effects have been investigated in the present paper, including the deformation characteristics (movement pattern, direction, displacement and velocity) earmarking the temporal evolution of the active block. Based on a practical creep model of a large rock slide, alert velocity thresholds for pre-alert, alert and emergency phases have been computed corresponding to the imminence of failure. The alert velocity thresholds are being proposed to be included as a part of an early-warning system of an emergency plan drawn up to minimize the adverse impact in the event of landslide failure. The emergency plan is intended to be implemented as a risk management tool by the relevant authorities of the Three Gorges Reservoir in the near future

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA

Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County, Three Gorges Reservoir

Soil thickness, intended as depth to bedrock, is a key input parameter for many environmental models. Nevertheless, it is often difficult to obtain a reliable spatially exhaustive soil thickness map in wide-area applications, and existing prediction models have been extensively applied only to test sites with shallow soil depths. This study addresses this limitation by showing the results of an application to a section of Wanzhou County (Three Gorges Reservoir Area, China), where soil thickness varies from 0 to ∼40 m. Two different approaches were used to derive soil thickness maps: a modified version of the geomorphologically indexed soil thickness (GIST) model, purposely customized to better account for the peculiar setting of the test site, and a regression performed with a machine learning algorithm, i.e., the random forest, combined with the geomorphological parameters of GIST (GIST-RF). Additionally, the errors of the two models were quantified, and validation with geophysical data was carried out. The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area: the mean absolute error was 10.68 m with the root-mean-square error (RMSE) of 12.61 m, and the frequency distribution residuals showed a tendency toward underestimation. In contrast, GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m. The derived soil thickness map could be considered a critical fundamental input parameter for further analyses

The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China)

Land use and land cover change can increase or decrease landslide susceptibility (LS) in the mountainous areas. In the hilly and mountainous part of southwestern China, land use and land cover change (LUCC) has taken place in the last decades due to infrastructure development and rapid economic activities. This development and activities can worsen the slope susceptible to sliding due to mostly the cutting of slopes. This study, taking Zhushan Town, Xuan'en County, as the study area, aims to evaluate the influence of land use and land cover change on landslide susceptibility at a regional scale. Spatial distribution of landslides was determined in terms of visual interpretation of aerial photographs and remote sensing images, supported by field surveys. Two types of land use and land cover (LUC) maps, with a time interval covering 21 years (1992–2013), were prepared: the first was obtained by the neural net classification of images acquired in 1992 and the second by the object-oriented classification of images in 2002 and 2013. Landslide-susceptible areas were analyzed using the logistic regression model (LRM) in which six influencing factors were chosen as the landslide susceptibility indices. In addition, the hydrologic analysis method was applied to optimize the partitioning of the terrain. The results indicated that the LUCC in the region was mainly the transformation from the grassland and arable land to the forest land, which is increased by 34.3 %. An increase of 1.9 % is shown in the area where human engineering activities concentrate. The comparison of landslide susceptibility maps among different periods revealed that human engineering activities were the most important factor in increasing LS in this region. Such results emphasize the requirement of a reasonable land use planning activity process

Quantitative risk assessment of slow-moving landslides from the viewpoint of decision-making: A case study of the Three Gorges Reservoir in China

A large number of landslides in the Three Gorges Reservoir area in China have been reactivated by reservoir impoundment since 2003. Many of them were activated as slow-moving landslides, which caused severe damages to buildings and also pose a significant risk for residents. In this study, a quantitative procedure has been proposed to analyze and assess the landslide risks to buildings and lives by considering the Outang landslide as an example. First, the landslide segmentation was done by considering the sliding history, macroscopic deformation, and monitoring data of displacement. The SLOPE/W and SEEP/W modules of the Geostudio software were used to establish the geotechnical model and analyze the seepage, respectively. Under different scenarios of annual reservoir regulation and return period of rainfall events as determined by Pearson type III (P3) distribution, failure probabilities of every segmented part were obtained with Monte Carlo simulation. Second, the vulnerability of the elements at risk was quantitatively estimated using a model that incorporated the landslide intensity and resistance of the exposed elements. Then, through the analysis and comparison of scenario-based landslide risks, the evolution of landslide risk influenced by different combinations of triggering factors (i.e., reservoir water level and rainfall) was examined. Finally, landslide risks before and after the implementation of several risk reduction alternatives were compared to determine the benefits of alternatives. The risk maps displayed that the risk was the greatest for the scenario of the rapid decline of the reservoir water level in combination with heavy rainfall; the corresponding total annual economic and population risks of the landslide were estimated in 96.88 million ¥ and 216 lives, respectively. In this scenario, the economic risk level of the west deformation zone was the highest, and the total population risk was the highest in the lower unit of the landslide. Results of benefit-cost analysis suggested that real-time monitoring and resident relocation seem to be the most effective alternatives for risk reduction concerning the Outang landslide