scholarly journals A Landslide Displacement Prediction Method with Iteration-Based Combined Strategy

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
L. Li ◽  
S. X. Zhang ◽  
Y. Qiang ◽  
Z. Zheng ◽  
S. H. Li ◽  
...  
Keyword(s):  
Engineering Application ◽  
Prediction Method ◽  
Three Gorges Reservoir Area ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Displacement Prediction ◽  
The Three Gorges ◽  
Landslide Displacement Prediction ◽  
Grey Models ◽  
Combined Strategy

Predicting landslide displacement is of great significance in geotechnical engineering. An iteration-based combined prediction method was proposed for predicting the landslide displacement in this paper. Firstly, the landslide displacement was predicted by 10 latest multivariable grey models, and then the final landslide displacement prediction value was obtained through an iteration-based combined strategy. Concurrently, the reliability of the quadratic programming-based combined prediction method (QPCPM) and the iteration-based combined prediction method (ICPM) was rigorously proved in this paper. In addition, the inapplicability conditions of the optimal weight-based combined prediction method (OWCPM) were pointed out. ICPM could be regarded as a simplified version of QPCPM. The Bazimen and Baishuihe landslides in the Three Gorges Reservoir area of China were used as numerical examples to elaborate the performance of ICPM. This paper also demonstrated the reliability of ICPM by considering the effects of rainfall and reservoir water level on landslide displacement. Overall, ICPM features in simple and easy calculation and has rosy engineering application prospects.

scholarly journals Point and Interval Predictions for Tanjiahe Landslide Displacement in the Three Gorges Reservoir Area, China

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yankun Wang ◽  
Huiming Tang ◽  
Tao Wen ◽  
Junwei Ma ◽  
Zongxing Zou ◽  
...  
Keyword(s):  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Support Vector ◽  
Three Gorges ◽  
Noise Variance ◽  
Displacement Prediction ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Landslide Displacement Prediction

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.

scholarly journals The Coupling Effect of Rainfall and Reservoir Water Level Decline on the Baijiabao Landslide in the Three Gorges Reservoir Area, China

Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Nenghao Zhao ◽  
Bin Hu ◽  
Qinglin Yi ◽  
Wenmin Yao ◽  
Chong Ma
Keyword(s):  
Water Level ◽  
Three Gorges Reservoir ◽  
Coupling Effect ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Area ◽  
The Three Gorges ◽  
Water Level Decline

Rainfall and reservoir level fluctuation are two of the main factors contributing to reservoir landslides. However, in China’s Three Gorges Reservoir Area, when the reservoir water level fluctuates significantly, it comes at a time of abundant rainfall, which makes it difficult to distinguish which factor dominates the deformation of the landslide. This study focuses on how rainfall and reservoir water level decline affect the seepage and displacement field of Baijiabao landslide spatially and temporally during drawdown of reservoir water level in the Three Gorges Reservoir Area, thus exploring its movement mechanism. The monitoring data of the landslide in the past 10 years were analyzed, and the correlation between rainfall, reservoir water level decline, and landslide displacement was clarified. By the numerical simulation method, the deformation evolution mechanism of this landslide during drawdown of reservoir water level was revealed, respectively, under three conditions, namely, rainfall, reservoir water level decline, and coupling of the above two conditions. The results showed that the deformation of the Baijiabao landslide was the coupling effect of rainfall and reservoir water level decline, while the latter effect is more pronounced.

Analysis of Bazimen Landslide Deformation Based on GPS

2011 ◽  
Vol 347-353 ◽  
pp. 435-439
Author(s):  
Peng Fei Tu ◽  
Sheng Chao Wu ◽  
Hong Tao Li
Keyword(s):  
Water Level ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Analysis Method ◽  
Mathematical Relationship ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Three Gorges ◽  
Landslide Deformation ◽  
Major Factors

Rainfall infiltration and reservoir water level fluctuation are major factors inducing slope deformation. Taking nearly three years data of Bazimen landslide in the Three Gorges Reservoir area and basing on the mathematical regression analysis method, a mathematical relationship among landslide deformation, water level and rainfall is established. And then the most adverse condition under the superposi¬tion of rainfall and reservoir water level is obtained.

scholarly journals An Integrated Approach for Displacement Prediction of Landslides Based on the Data Mining and VMD-FOA-BPNN Model

2021 ◽  
Author(s):  
Fasheng Miao ◽  
Xiaoxu Xie ◽  
Yiping Wu ◽  
Linwei Li ◽  
Weiwei Zhan
Keyword(s):  
Data Mining ◽  
Prediction Models ◽  
Integrated Approach ◽  
Three Gorges Reservoir Area ◽  
Fruit Fly Optimization Algorithm ◽  
Reservoir Water ◽  
Displacement Prediction ◽  
Triggering Factors ◽  
Landslide Displacement Prediction ◽  
Combining Data

Abstract Landslide prediction is important for mitigating geohazards but is very challenging. The fluctuation of reservoir water level and rainfall are the main external triggering factors controlling the deformation of riverine landslides. In this paper, the Baishuihe landslide in the Three Gorges Reservoir area, which has a typical “step-like” behavior, is taken as the study case, and an integrated approach for landslide displacement prediction combining data mining and Variational Mode Decomposition, Fruit Fly Optimization Algorithm, Back Propagation Neural Network (VMD-FOA-BPNN) is proposed. Nine triggering factors including the reservoir level and rainfall are extracted. First, triggering factors and monthly velocity of the landslide are clustered into several categories by Two-step Clustering (TSC). Then, Apriori algorithm is used to mine the association rules between triggering factors and monthly velocity, and comprehensive contribution of each factor is calculated based on the data mining results. Next, the displacement of monitoring point ZG93 and triggering factors are decomposed by VMD based on the time series analysis of the landslide. Last, the trend term displacement is trained and predicted by the subsection functions, and FOA-BPNN models are used to train and predict the periodic and random term. The prediction results show that, compared with the current popular prediction models, the proposed model can effectively improve the prediction accuracy, which has high practicability and application value in the study of landslide displacement prediction.

scholarly journals Phreatic line calculation and stability analysis of slopes under the combined effect of reservoir water level fluctuations and rainfall

2017 ◽  
Vol 54 (5) ◽  
pp. 631-645 ◽  
Author(s):  
Guanhua Sun ◽  
Yongtao Yang ◽  
Shengguo Cheng ◽  
Hong Zheng
Keyword(s):  
Water Level ◽  
Three Gorges Reservoir ◽  
Combined Effect ◽  
Water Level Fluctuations ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Area ◽  
The Three Gorges

Rainfall and reservoir water level fluctuations are the main external factors of landslides in the Three Gorges Reservoir area. To improve the analysis of slope stability under the combined effect of reservoir water level fluctuations and rainfall, a simplified method for phreatic line calculation of slopes is proposed in this study. Based on the obtained phreatic line, the expression of normal stress on the sliding surface of the slope under the hydrodynamic forces is deduced, and a global analysis method to solve the slope safety factor under hydrodynamic force is proposed. Finally, the safety evolution of a slope in the Three Gorges Reservoir area is studied under the combined effect of reservoir water level fluctuations and rainfall.

Analysis on Deformation Failure Mechanism of Woshaxi Landslide in the Three Gorges Reservoir Area

2012 ◽  
Vol 594-597 ◽  
pp. 498-501
Author(s):  
Shu Qiang Lu ◽  
Qing Lin Yi ◽  
Wu Yi
Keyword(s):  
Water Level ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Three Gorges ◽  
Landslide Deformation ◽  
Main Factors ◽  
The Right ◽  
Water Level Decline

The Woshaxi landslide is located on the right bank of Qinggan river which is a tributary of the Yangtze River, which continuously and seriously developed after the impoundment of Three Gorges. Surface investigation showed that there were shearing, tensing and crush-pressing fissures. The monitoring data showed that displacement of lower part was bigger than that of upper, and the sliding because of the reservoir water level decline. The landslide mechanism was towing forward and pushing backward, and towing happened mainly in the prophase, while pushing happened mainly in the anaphase. Heavy rainfall and water level drawdown are the main factors of landslide deformation.

Landslide Displacement Prediction Based on Variational Mode Decomposition and MIC-GWO-LSTM Model

2021 ◽  
Author(s):  
Taorui Zeng ◽  
Hongwei Jiang ◽  
Qingli Liu ◽  
Kunlong Yin
Keyword(s):  
Prediction Model ◽  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Soil Conditions ◽  
Three Gorges ◽  
Displacement Prediction ◽  
Reservoir Area ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Landslide Displacement Prediction

Abstract Landslide displacement prediction is essential to establish the early warning system (EWS). According to the dynamic characteristics of landslide evolution and the shortcomings of the traditional static prediction model, a dynamic prediction model of landslide displacement based on long short-term memory (LSTM) neural networks was proposed. Meanwhile, the Variational modal decomposition (VMD) theory was used to decompose the cumulative displacement and triggering factors, which not only give clear physical meaning to each displacement subsequence, but also closely connect the rock and soil conditions with the influence of external factors. Besides, the maximum information coefficient (MIC) was used to sort the redundant features. The LSTM is a dynamic model that can remember historical information and apply it to the current output. The hyperparameters of the LSTM model was optimized by the Grey wolf optimizer (GWO), and the dynamic one-step prediction was carried out for each displacement. All the predicted values were superimposed to complete the displacement prediction based on the time series model. The Tangjiao landslide in the Three Gorges Reservoir area (TGRA), China, was taken as a case study. The displacement data of monitoring sites GPS06 had step-like characteristics. Measured data from March 2007 to December 2016 were selected for analysis. The results indicate that the root mean square error (RMSE) of the test set and validation set are 23.240 mm and 64.714 mm, respectively, and the coefficient of determination (R2) are 0.997 and 0.971, respectively. This model provides a new idea and exploration for the displacement prediction of step-like characteristics landslide in the Three Gorges Reservoir area.

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

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhaodan Cao ◽  
Jun Tang ◽  
Xiaoer Zhao ◽  
Yonggang Zhang ◽  
Bin Wang ◽  
...  
Keyword(s):  
Water Level ◽  
Failure Mechanism ◽  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Landslide Stability ◽  
The Three Gorges ◽  
Colluvial Landslide

The stability of the reservoir bank landslide is affected by a variety of external factors, and the fluctuation of reservoir water level is one of the important influencing factors. The Erdaohe landslide is a typically colluvial landslide in the Three Gorges Reservoir area with periodic reservoir water level fluctuations. According to landslide displacement data, the displacement of the Erdaohe landslide exhibits the significantly stepwise feature. Its failure mechanism was analyzed using strength reduction method by the FLAC3D package in the case of reservoir water level changes. The results indicate that the hydrodynamic pressure has an important impact on the initialization of the landslide failure. When reservoir water level rises rapidly or maintains constant at the lower level, the landslide stability would be higher. When the reservoir water level decreases rapidly or maintains constant at the higher level, the landslide stability will be smaller. When the reservoir water level was in the lowest elevation, the factor of safety (FS) reached the minimum value of 1.11. Findings in this paper can provide guidelines for the risk assessment of colluvial landslides.

scholarly journals Characterizing the Development Pattern of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges Reservoir

2021 ◽  
Vol 13 (2) ◽  
pp. 224
Author(s):  
Xin Liang ◽  
Lei Gui ◽  
Wei Wang ◽  
Juan Du ◽  
Fei Ma ◽  
...  
Keyword(s):  
Time Variable ◽  
Development Pattern ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Three Gorges ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Colluvial Landslide

Since the impoundment of the Three Gorges Reservoir (TGR) in June 2003, the fluctuation of the reservoir water level coupled with rainfall has resulted in more than 2500 landslides in this region. Among these instability problems, most colluvial landslides exhibit slow-moving patterns and pose a significant threat to local people and channel navigation. Advanced monitoring techniques are therefore implemented to investigate landslide deformation and provide insights for the subsequent countermeasures. In this study, the development pattern of a large colluvial landslide, locally named the Ganjingzi landslide, is analyzed on the basis of long-term monitoring. To understand the kinematic characteristics of the landslide, an integrated analysis based on real-time and multi-source monitoring, including the global navigation satellite system (GNSS), crackmeters, inclinometers, and piezometers, was conducted. The results indicate that the Ganjingzi landslide exhibits a time-variable response to the reservoir water fluctuation and rainfall. According to the supplement of community-based monitoring, the evolution of the landslide consists of three stages, namely the stable stage before reservoir impoundment, the initial movement stage of retrogressive failure, and the shallow movement stage with stepwise acceleration. The latter two stages are sensitive to the drawdown of reservoir water level and rainfall infiltration, respectively. All of the monitoring approaches used in this study are significant for understanding the time-variable pattern of colluvial landslides and are essential for landslide mechanism analysis and early warning for risk mitigation.

Sign in / Sign up

Export Citation Format

Share Document