Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods : A case study in Saeen Slope, Azerbaijan province, Iran

Auteur(s) et Affiliation(s)

ALIMOHAMMADLOU, Y.

Dept. of Civil Engineering, Islamic Azad Univ., Zanjan, Iran, Republique Islamique d'

NAJAFI, A.

Dept. of Industrial Engineering, Islamic Azad Univ., Zanjan, Iran, Republique Islamique d'

GOKCEOGLU, C.

Hacettepe Univ., Geological Engineering Dept., Beytepe, Ankara, Turquie

Description :
The aim of this study is to propose a novel approach utilizing artificial neural network and fuzzy clustering methods for landslide frequency estimation. This study also investigates the 2005 Saeen, Iran landslide triggered by prolonged heavy rainfall that affected groundwater levels, and introduces a methodology to estimate the date range of the next probable landslide. Based on the interpretation of the triggering factor and failure mechanism, the Saeen landslide was induced by the prolonged rainfall behavior and resultant deep infiltration of water between the years 2002 and 2005. The results of this investigation revealed that the failure probability will likely increase in the next precipitation periods and the saturation rate will be high in August and September of 2017 and 2018, resulting in landslides. In conclusion, this method is only used for the heavy precipitation as the triggering factor to estimate and analyze the next potential landslide.

Type de document :
Article de périodique

Source :
Catena (Giessen), issn : 0341-8162, 2014, vol. 120, p. 149-162, nombre de pages : 14, Références bibliographiques : 1 p.

Date :
2014

Editeur :
Pays édition : Allemagne, Cremlingen-Destedt, Catena

Langue :
Anglais