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Environmental Earth Sciences

, Volume 72, Issue 6, pp 1925–1938 | Cite as

Landslide susceptibility analysis based on ArcGIS and Artificial Neural Network for a large catchment in Three Gorges region, China

  • Renneng BiEmail author
  • Markus Schleier
  • Joachim Rohn
  • Dominik Ehret
  • Wei Xiang
Original Article

Abstract

A landslide susceptibility map is very important and necessary to efficiently prevent and mitigate the losses brought by natural hazard for a large area. For the purpose of landslide susceptibility analysis for the whole Xiangxi catchment (3,209 km2), Artificial Neural Network (ANN) analysis was applied as the main method. The whole catchment was divided into two parts: the training area and the implementation area. The backwater area (559 km2) of Xiangxi catchment was used as the training area for the ANN method. In the training area the correlations between the landslide distribution and its causative factors, which includes lithology, slope angle, slope curvature and river network, have been analyzed based on the geological map and digital elevation model (DEM). The back-propagation training algorithm in ANN was selected to train the sample data from the training area, which were composed of input data (causative factors) and target output data (landslide occurrence), in order to find the correlations between them. Based on these correlations and input data in the implementation area (causative factors), the network output data were obtained for the implementation area. In the end, a map of landslide susceptibility, which was established by network output data, was presented for Xiangxi catchment. ArcGIS was applied to extract and quantify input information from a DEM for susceptibility analysis and also to present the result visually. As a result, a landslide susceptibility map, in which 70 % of all landslides are rightly classified in the training area (backwater area), was created for Xiangxi catchment.

Keywords

Three Gorges Reservoir Landslide susceptibility Landslide causative factors Artificial Neural Network ArcGIS 

Notes

Acknowledgments

The studies were carried out as a part of the Yangtze-Project which is supported by the German Federal Ministry of Education and Research (BMBF). The authors would like to thank the working group of Prof. Xiang Wei from China University of Geosciences (Wuhan) and the students from Prof. Joachim Rohn in GeoZentrum Nordbayern (FAU) in Germany for their intensive field work in China.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Renneng Bi
    • 1
    • 3
    Email author
  • Markus Schleier
    • 1
  • Joachim Rohn
    • 1
  • Dominik Ehret
    • 2
  • Wei Xiang
    • 3
  1. 1.GeoZentrum Nordbayern, University of Erlangen-NürnbergErlangenGermany
  2. 2.State Office for Geology, Resources and Mining of Baden-WürttembergFreiburg im BreisgauGermany
  3. 3.Three Gorges Reservoir Center for Geo-hazard, Ministry of Education, China University of GeosciencesWuhanChina

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