scholarly journals Quantitative Risk Analysis of a Rainfall-Induced Complex Landslide in Wanzhou County, Three Gorges Reservoir, China

2020 ◽  
Vol 11 (3) ◽  
pp. 347-363
Author(s):  
Lili Xiao ◽  
Jiajia Wang ◽  
Yanbo Zhu ◽  
Jun Zhang
Keyword(s):  
Risk Analysis ◽  
Three Gorges Reservoir ◽  
Three Gorges ◽  
Quantitative Risk Analysis ◽  
Complex Landslide

A real-time operation of the Three Gorges Reservoir with flood risk analysis

2015 ◽  
Vol 16 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Yixiang Sun ◽  
Deshan Tang ◽  
Huichao Dai ◽  
Pan Liu ◽  
Yifei Sun ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Flood Risk ◽  
Reservoir Operation ◽  
Three Gorges Reservoir ◽  
Three Gorges ◽  
Hydropower Generation ◽  
Inference System ◽  
The Three Gorges Reservoir ◽  
Flood Season ◽  
The Three Gorges

Risk analysis is essential to reservoir operation. In this study, a new analysis for reservoir operation is proposed to enhance the utilization rate of the flood water from the Three Gorges Reservoir (TGR) during the flood season. Based on five scenarios of hydrology forecasting with the adaptive neuro-fuzzy inference system (ANFIS), a multi-objective optimum operation was implemented employing the risk control constraints of the genetic algorithm (GA) for the TGR. The results of this analysis indicated that the optimum hydropower generation was 5.7% higher than the usual operating hydropower generation, which suggested that, during flood season, it would be beneficial to increase hydropower generation from reservoirs, while maintaining a safe degree of flood risk.

Quantitative risk analysis for landslides: the case of the Three Gorges area, China

Landslides ◽  
2014 ◽  
Vol 12 (5) ◽  
pp. 943-960 ◽  
Author(s):  
Ling Peng ◽  
Suning Xu ◽  
Jinwu Hou ◽  
Junhuan Peng
Keyword(s):  
Risk Analysis ◽  
Three Gorges ◽  
Quantitative Risk Analysis ◽  
Three Gorges Area ◽  
The Three Gorges

scholarly journals Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Junwei Ma ◽  
Xiaoxu Niu ◽  
Huiming Tang ◽  
Yankun Wang ◽  
Tao Wen ◽  
...  
Keyword(s):  
Probability Density ◽  
Computational Intelligence ◽  
Three Gorges Reservoir ◽  
Three Gorges Reservoir Area ◽  
Three Gorges ◽  
Displacement Prediction ◽  
Density Prediction ◽  
The Three Gorges ◽  
Complex Landslide ◽  
Intelligence Approach

Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density prediction model of landslide displacement and quantify the associated predictive uncertainties. The hybrid computational intelligence approach consists of two steps: first, the input variables are selected through copula analysis; second, kernel-based support vector machine quantile regression (KSVMQR) is employed to perform density prediction. The copula-KSVMQR approach is demonstrated through a complex landslide in the Three Gorges Reservoir Area (TGRA), China. The experimental study suggests that the copula-KSVMQR approach is capable of construction density prediction by providing full probability density distributions of the prediction with perfect performance. In addition, different types of predictions, including interval prediction and point prediction, can be derived from the obtained density predictions with excellent performance. The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. Given the satisfactory performance, the presented copula-KSVMQR approach shows a great ability to predict landslide displacement.

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