Earticle

초록

영어

Since the initial impoundment of the Three Gorges Reservoir in June 2003 and approximately 30 m of reservoir level fluctuation, numerous preexisting landslides have been reactivated. To mitigate disastrous landslides, the Baishuihe landslide in the Three Gorges region was selected as a case study in predicting such displacement using the monitoring dataand a radial basis function-support vector machine (RBF-SVM) model.The landslide displacement was strongly influenced by periodic precipitation, reservoir level and groundwater level fluctuations. Primary landslide influencing factors were used as independent variables to predict the displacement using several kernel function types including sigmoid function, polynomial function, and RBF based on SVM model.Prediction results demonstrated that the RBF-SVM with the optimal parameters c, ε and r of 170, 0.05 and 0.04 can provide the best predictive accuracy, with the maximum and minimum absolute error values of 9.84 and 0.47 mm, respectively.

목차

Abstract
 1. Introduction
 2. Description of the Baishuihe Landslide
 3. RBF-SVM
 4. Landslide Displacement Prediction
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• Fu Ren [ School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079,China ]
• Xueling Wu [ Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074,China ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605