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Rainfall and Earthquake-induced Landslide Susceptibility Assessment Using Gis and Artificial Neural Network : Volume 12, Issue 8 (31/08/2012)

By Li, Y.

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Book Id: WPLBN0004017952
Format Type: PDF Article :
File Size: Pages 11
Reproduction Date: 2015

Title: Rainfall and Earthquake-induced Landslide Susceptibility Assessment Using Gis and Artificial Neural Network : Volume 12, Issue 8 (31/08/2012)  
Author: Li, Y.
Volume: Vol. 12, Issue 8
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Zhou, G., Tang, C., Zheng, L., Li, Y., & Chen, G. (2012). Rainfall and Earthquake-induced Landslide Susceptibility Assessment Using Gis and Artificial Neural Network : Volume 12, Issue 8 (31/08/2012). Retrieved from

Description: Department of Civil and Structural Engineering, Kyushu University, Fukuoka, Japan. 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%.

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

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