Evaluation methods of regional bank slope stability based on geographic information systems and integrated information model
Vol 5, Issue 1, 2022
VIEWS - 332 (Abstract) 243 (PDF)
Abstract
Through the combination of the geographic information systems (GIS) and the integrated information model, the stability of regional bank slope was comprehensively evaluated. First, a regional bank slope stability evaluation index system was established through studying seven selected factors (slope grade, slope direction, mountain shadow, elevation, stratigraphic lithology, geological structure and river action) that have an impact on the stability of the slope. Then, each factor was rasterized by GIS. According to the integrated information model, the evaluation index distribution map based on rasterized factors was obtained to evaluate the stability of the regional bank slope. Through the analysis of an actual project, it was concluded that the geological structure and stratigraphic lithology have a significant impact on the evaluation results. Most of the research areas were in the relatively low stable areas. The low and the relatively low stable areas accounted for 15.2% and 51.5% of the total study area respectively. The accuracy of slope evaluation results in the study area reached 95.41%.
Keywords
Full Text:
PDFReferences
1. Ding Z, Zhang F, Yang F. Analysis of the large res-ervoir landslide stability influenced by rainfall. Science Technology and Engineering 2013; 13(10): 2736–2740.
2. Hungr O. An extension of Bishop’s simplified method of slope stability analysis to three dimen-sions. Géotechnique 1987; 37(1): 155–156.
3. Xie M, Esaki T. Stability analysis of 3D slope based on GIS spatial database. Chinese Journal of Rock Mechanics and Engineering 2002; 21(10): 1494–1499.
4. Zhou G, Li T. Slope aseismic stability analysis method based on static and dynamic finite elements. Rock and Soil Mechanics 2010; 31(7): 2303–2308.
5. He P, Tong L, Guo Z, et al. Evaluation research on the landslide disaster liability in Zhada region of Tibet. Science Technology and Engineering 2016; 16(25): 193–200.
6. Yang H, Wu B, Wang L. Application of PSO-RBF coupling model based on chaos theory in forecast displacement of landslide. Science Technology and Engineering 2013; 13(30): 9118–9121, 9126.
7. Sun G. Landslide sliding force prediction based on ARMA model. Science Technology and Engineer-ing 2014; 14(36): 17–20, 28.
8. Liu Y, Yin K, Liu B. Application of logistic regres-sion and artificial neural networks in spatial as-sessment of landslide hazards. Hydrogeology and Engineering Geology 2010; 37(5): 92–96.
9. Li Y, Chen W, Li X, et al. Stability assessment of rock slope based on fuzzy neural network. Journal of Wuhan University of Technology 2013; 35(1): 113–118.
10. Xue X, Zhang W, Liu H. Evaluation of slope stabil-ity based on genetic algorithm and fuzzy neural network. Rock and Soil Mechanics 2007; 28(12): 2643–2648.
11. Deng H, He Z, Chen Y, et al. Application of infor-mation quantity model to hazard evaluation of ge-ological disaster in mountainous region environ-ment: A case study of Luding County, Sichuan Province. Journal of Natural Disasters 2014; 23(2): 67–76.
12. An K, Niu R. Landslide susceptibility assessment using support vector machine based on weighted-information model. Journal of Yangtze River Scientific Research Institute 2016; 33(8): 47–51, 58.
13. Tan Y, Guo D, Bai B, et al. Geological hazard risk assessment based on information quantity model in Fuling District, Chongqing City, China. Journal of Geo-information Science 2015; 17(12): 1554–1562.
14. Xie M, Cai M. The theory and practice of infor-mation slope engineering. Beijing: Science Press; 2005.
15. Yin K, Zhu L. Landslide hazard zonation and ap-plication of GIS. Earth Science Frontiers 2001; 8(2): 279–284.
16. Zhang G, Yin K, Liu C, et al. The hazard zoning of landslide supported by GIS in Xunyang region of Shanxi Province. The Chinese Journal of Geological Hazard and Control 2003; 14(4): 39–43.
17. Hu X. Water effect to slope stable and prevention. Metal Mine 2009; 45(S1): 736–739.
18. Tang C, Zhu J, Qi X. Landslide hazard assessment of the 2008 Wenchuan earthquake: A case study in Beichuan area. Canadian Geotechnical Journal 2011; 48(1): 128–145.
19. Guzzetti F, Carrara A, Cardinali M, et al. Landslide hazard evaluation: A review of current techniques and their application in a multiscale study, central Italy. Geomorphology 1999; 31(1-4): 181–216.
20. Gorsevski P, Jankowski P. Discerning landslide susceptibility using rough sets. Computers Envi-ronment & Urban Systems 2008; 32(1): 53–65.
DOI: https://doi.org/10.24294/jgc.v5i1.1411
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Guo Yu, Mowen Xie, Yong Li
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This site is licensed under a Creative Commons Attribution 4.0 International License.