Volume 5, Issue 2 (4-2012)                   2012, 5(2): 1269-1286 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

soori S. Landslide hazard zonation using artificial neural networks A case study: Keshvari watershed (Nozhiyan). Journal of Engineering Geology 2012; 5 (2) :1269-1286
URL: http://jeg.khu.ac.ir/article-1-374-en.html
1- , soorisalman@yahoo.com
Abstract:   (12958 Views)
The Keshvari watershed is located at south east of Khorramabad city in Lorestan province. This area is one part of the folded Zagros zone based on structural geology classification. By consider the type of geological formations, topographic conditions and its area, this watershed is very unstable and capable for occurring landslide. In this study, artificial neural network (ANN) with structure of multi-layer percepteron and Back Propagation learning algorithm used for zonation of landslide risk. The results of ANN showed the final structure of 9-11-1 for zonation of landslide risk in Keshvari watershed. According this zonation, 23.81, 7.53, 6.49, 18.68 and 43.47 percent of area are located in very low, low, moderate, high and very high risk classes, respectively.
Full-Text [PDF 2844 kb]   (3416 Downloads)    
Type of Study: Original Research | Subject: En. Ecosystem
Accepted: 2016/10/5 | Published: 2016/10/5

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Engineering Geology

Designed & Developed by : Yektaweb