ASSESSING LANDSLIDE HAZARD USING ARTIFICIAL NEURAL NETWORK: CASE STUDY OF MAZANDARAN, IRAN
Farzad FARROKHZAD1, Asskar Janalizadeh CHOOBBASTI1, Amin BARARI2 & Lars Bo IBSEN2
1Department of Civil Engineering, Babol University of Technology, Babol, Mazandaran, Iran
2 Department of Civil Engineering, Aalborg University, Sohngårdsholmsvej 57, 9000 Aalborg, Aalborg, Denmark
Corresponding author: Email: ab@civil.aau.dk; amin78404@yahooo.com
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Abstract
Investigations of soil failures are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. From the studies of field case records at least two types of soil failures have been distinguished, namely "shear failure" which is main concentration of the current research and "liquefaction failure". Shear failures along shear planes occur when the shear stress along the sliding surfaces exceed the effective shear strength. These slides have been referred to as landslide. An expert system based on artificial neural network has been developed for use in the stability evaluation of slopes under various geological conditions and engineering requirements. The Artificial neural network model of this research uses slope characteristics as input and leads to the output in form of the probability of failure and factor of safety. It can be stated that the trained neural networks are capable of predicting the stability of slopes and safety factor of landslide hazard in study area with an acceptable level of confidence. Landslide hazard analysis and mapping can provide useful information for catastrophic loss reduction, and assist in the development of guidelines for sustainable land use planning. The analysis is used to identify the factors that are related to landslides and to predict the landslide hazard in the future based on such a relationship.
Keywords:
- Landslides
- Expert
- system
- Artificial
- neural
- network
- Geology
- Mazandaran.
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© 2011 by the author(s). Licensee CJEES, Carpathian Association of Environment and Earth Sciences. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
How to cite
Farzad FARROKHZAD, Asskar Janalizadeh CHOOBBASTI, Amin BARARI & Lars Bo IBSEN (2011). ASSESSING LANDSLIDE HAZARD USING ARTIFICIAL NEURAL NETWORK: CASE STUDY OF MAZANDARAN, IRAN
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