Displacement prediction in colluvial landslides, Three Gorges Reservoir, China

The prediction of active landslide displacement is a critical component of an early warning system and helps prevent property damage and loss of human lives. For the colluvial landslides in the Three Gorges Reservoir, the monitored displacement, precipitation, and reservoir level indicated that the characteristics of the deformations were closely related to the seasonal fluctuation of rainfall and reservoir level and that the displacement curve versus time showed a stepwise pattern. Besides the geological conditions, landslide displacement also depended on the variation in the influencing factors. Two typical colluvial landslides, the Baishuihe landslide and the Bazimen landslide, were selected for case studies. To analyze the different response components of the total displacement, the accumulated displacement was divided into a trend and a periodic component using a time series model. For the prediction of the periodic displacement, a back-propagation neural network model was adopted with selected factors including (1) the accumulated precipitation during the last 1-month period, (2) the accumulated precipitation over a 2-month period, (3) change of reservoir level during the last 1 month, (4) the average elevation of the reservoir level in the current month, and (5) the accumulated displacement increment during 1 year. The prediction of the displacement showed a periodic response in the displacement as a function of the variation of the influencing factors. The prediction model provided a good representation of the measured slide displacement behavior at the Baishuihe and the Bazimen sites, which can be adopted for displacement prediction and early warning of colluvial landslides in the Three Gorges Reservoir.

[1]  Leng Wuming,et al.  Nonlinear Combination Predicting Based on Support Vector Machines for Landslide Deformation , 2007 .

[2]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[3]  Denys Brunsden,et al.  A critical assessment of the sensitivity concept in geomorphology , 2001 .

[4]  F. Mayoraz,et al.  Neural Networks for Slope Movement Prediction , 2002 .

[5]  William Murphy,et al.  Patterns of movement in rotational and translational landslides , 2002 .

[6]  Zhitao Huo,et al.  The July 14, 2003 Qianjiangping landslide, Three Gorges Reservoir, China , 2004 .

[7]  Shi-Sheng Li,et al.  Study on deformation prediction of landslide based on genetic algorithm and improved BP neural network , 2010, Kybernetes.

[8]  David N. Petley,et al.  The mechanics of deep‐seated landslides , 1997 .

[9]  C. F. Lee,et al.  Frequency–volume relation and prediction of rainfall-induced landslides , 2001 .

[10]  B. Voight,et al.  A method for prediction of volcanic eruptions , 1988, Nature.

[11]  Setsuo Hayashi,et al.  On the Forecast of Time to Failure of Slope , 1988 .

[12]  Zhitao Huo,et al.  Three Gorges Reservoir, China , 2004 .

[13]  James D. Hamilton Time Series Analysis , 1994 .

[14]  B. Voight,et al.  A Relation to Describe Rate-Dependent Material Failure , 1989, Science.

[15]  Kyoji Sassa,et al.  Displacement Monitoring and Physical Exploration on the Shuping Landslide Reactivated by Impoundment of the Three Gorges Reservoir, China , 2005 .

[16]  Fabio Rocca,et al.  Dynamics of Slow-Moving Landslides from Permanent Scatterer Analysis , 2004, Science.

[17]  Huang Xue-bin,et al.  RESEARCH ON SPACE-TIME EVOLUTION LAWS AND EARLY WARNING-PREDICTION OF LANDSLIDES , 2008 .

[18]  W. Sijing Analysis of displacement dynamic features of colluvial landslide induced by rainfall , 2005 .

[19]  B. Pradhan,et al.  Comparison and Validation of Landslide Susceptibility Maps Using an Artificial Neural Network Model for Three Test Areas in Malaysia , 2010 .

[20]  W. M. Brown,et al.  Real-Time Landslide Warning During Heavy Rainfall , 1987, Science.

[21]  Jeffrey A. Coe,et al.  Seasonal movement of the Slumgullion landslide determined from Global Positioning System surveys and field instrumentation, July 1998–March 2002 , 2003 .

[22]  C. Fidelibus,et al.  On the prediction of the time of occurrence of a slope failure: a review , 2004 .

[23]  B. Pradhan,et al.  Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models , 2010 .

[24]  Zhou Yu-cai Analysis of relationship between landslide and rainfall in Jiangxi Province , 2008 .

[25]  Philippa J. Mason,et al.  Landslide hazard assessment in the Three Gorges area of the Yangtze river using ASTER imagery: Zigui–Badong , 2004 .

[26]  Kyoji Sassa,et al.  Monitoring, Prediction and Early Warning , 2009 .

[27]  J. Corominas,et al.  Reconstructing recent landslide activity in relation to rainfall in the Llobregat River basin, Eastern Pyrenees, Spain , 1999 .

[28]  Hecht-Nielsen Theory of the backpropagation neural network , 1989 .

[29]  Wang Jian Quantitative prediction of landslide using S-curve , 2003 .

[30]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .