Global sensitivity analysis of the hydraulic parameters of the reservoir colluvial landslides in the Three Gorges Reservoir area, China

被引:22
作者
Wang, Yankun [1 ]
Huang, Jinsong [2 ]
Tang, Huiming [1 ,3 ]
机构
[1] China Univ Geosci, Fac Engn, Wuhan 430074, Hubei, Peoples R China
[2] Univ Newcastle, Discipline Civil Surveying & Environm Engn, Prior Res Ctr Geotech Sci & Engn, Callaghan, NSW 2308, Australia
[3] China Univ Geosci, Gorges Res Ctr Geohazards, Minist Educ, Wuhan 430074, Hubei, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Global sensitivity analysis; Reservoir colluvial landslide; Hydraulic parameters; PAWN; Three Gorges Reservoir area; WATER; MODEL; CONDUCTIVITY; PROBABILITY; UNCERTAINTY; OUTPUT; SOIL;
D O I
10.1007/s10346-019-01290-9
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
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.
引用
收藏
页码:483 / 494
页数:12
相关论文
共 38 条
[1]   Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change [J].
Almeida, Susana ;
Holcombe, Elizabeth Ann ;
Pianosi, Francesca ;
Wagener, Thorsten .
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2017, 17 (02) :225-241
[2]  
[Anonymous], 2012, Handbook of Hydrogeology, V2nd ed.
[3]  
[Anonymous], 1994, INTRO BOOTSTRAP
[4]   Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization [J].
Bui, Dieu Tien ;
Tran Anh Tuan ;
Nhat-Duc Hoang ;
Nguyen Quoc Thanh ;
Duy Ba Nguyen ;
Ngo Van Liem ;
Pradhan, Biswajeet .
LANDSLIDES, 2017, 14 (02) :447-458
[5]   DEVELOPING JOINT PROBABILITY-DISTRIBUTIONS OF SOIL-WATER RETENTION CHARACTERISTICS [J].
CARSEL, RF ;
PARRISH, RS .
WATER RESOURCES RESEARCH, 1988, 24 (05) :755-769
[6]   Uncertainty quantification of the fracture properties of polymeric nanocomposites based on phase field modeling [J].
Hamdia, Khader M. ;
Msekh, Mohammed A. ;
Silani, Mohammad ;
Nam Vu-Bac ;
Zhuang, Xiaoying ;
Trung Nguyen-Thoi ;
Rabczuk, Timon .
COMPOSITE STRUCTURES, 2015, 133 :1177-1190
[7]   Variance-based sensitivity analysis of the probability of hydrologically induced slope instability [J].
Hamm, N. A. S. ;
Hall, J. W. ;
Anderson, M. G. .
COMPUTERS & GEOSCIENCES, 2006, 32 (06) :803-817
[8]   UNCERTAINTY AND SENSITIVITY ANALYSIS TECHNIQUES FOR USE IN PERFORMANCE ASSESSMENT FOR RADIOACTIVE-WASTE DISPOSAL [J].
HELTON, JC .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 1993, 42 (2-3) :327-367
[9]   Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map [J].
Ilia, Ioanna ;
Tsangaratos, Paraskevas .
LANDSLIDES, 2016, 13 (02) :379-397
[10]  
Jian WX, 2014, ROCK SOIL MECH, V35, P3517