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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 8
Nat. Hazards Earth Syst. Sci., 12, 2719–2729, 2012
https://doi.org/10.5194/nhess-12-2719-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Documentation and monitoring of landslides and debris flows...

Nat. Hazards Earth Syst. Sci., 12, 2719–2729, 2012
https://doi.org/10.5194/nhess-12-2719-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 31 Aug 2012

Research article | 31 Aug 2012

Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

Y. Li1, G. Chen1, C. Tang2, G. Zhou3, and L. Zheng1 Y. Li et al.
  • 1Department of Civil and Structural Engineering, Kyushu University, Fukuoka, Japan
  • 2State Key Laboratory of Geo-Hazard Prevention, Chengdu University of Technology, Chengdu, China
  • 3Nishi-Nippon Institute of Technology, Fukuoka, Japan

Abstract. A GIS-based method for the assessment of landslide susceptibility in a selected area of Qingchuan County in China is proposed by using the back-propagation Artificial Neural Network model (ANN). Landslide inventory was derived from field investigation and aerial photo interpretation. 473 landslides occurred before the Wenchuan earthquake (which were thought as rainfall-induced landslides (RIL) in this study), and 885 earthquake-induced landslides (EIL) were recorded into the landslide inventory map. To understand the different impacts of rainfall and earthquake on landslide occurrence, we first compared the variations between landslide spatial distribution and conditioning factors. Then, we compared the weight variation of each conditioning factor derived by adjusting ANN structure and factors combination respectively. Last, the weight of each factor derived from the best prediction model was applied to the entire study area to produce landslide susceptibility maps.

Results show that slope gradient has the highest weight for landslide susceptibility mapping for both RIL and EIL. The RIL model built with four different factors (slope gradient, elevation, slope height and distance to the stream) shows the best success rate of 93%; the EIL model built with five different factors (slope gradient, elevation, slope height, distance to the stream and distance to the fault) has the best success rate of 98%. Furthermore, the EIL data was used to verify the RIL model and the success rate is 92%; the RIL data was used to verify the EIL model and the success rate is 53%.

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