Landslide Hazard Mapping Using the Artificial Neural Network a Part of Haraz Watershed

Document Type : Research

Authors

1 Department of Range and Watershed Management Engineering, faculty of Agriculture and Natural Resources, Lorestan University

2 Associate Prof., Faculty of Natural Resources, Tarbiat Modares University

3 Associate Prof., Biophysics Department, Faculty of Biological Sciences, Tarbiat Modares University

Abstract

A large part of Iran's formed mountainous areas, so each year, landslides cause damage to structures, residential areas and forests, creating sedimentation, mud floods and finally cause filling reservoirs. Since forecasting of the landslide occurrence is out of human knowledge in both temporally and spatially, so the landslide zoning is considered in order to be shown how much a mountain slope is susceptible to a mass movement. In this study, nine factors including slope percent and aspect, geology, precipitation, distance from the road and the river and faults, land use and elevation were used. The purpose of this study is to determine the most effective factor on landslide occurrence and preparation of landslide susceptibility map in a Part of Haraz Watershed. In this study for determining the most effective factor influencing on landslide occurrence and finally preparing of the landslide susceptibility map Analytic Hierarchy Process (AHP) and Artificial Neural Network (ANN) were used. From seventy eight points of slide and seventy eight points of un-slide determined in this area, seventy percent used for modeling and thirty percent for testing. Firstly, for providing aforementioned layers, Geographic Information System (GIS) was applied and then each of classes specified in every layer was valued using frequency ratio. In this study, for network training used Back-Propagation (BP) algorithm and sigmoid function. The results of the Analytic Hierarchy Process showed that slope is the most importance factor among studied factors. The results of the Artificial Neural network showed that structure 9-14-1 whit learning rate 0.2 is optimal structure and Root Mean Square Error is 0.051. Accuracy of network in training and testing phase was equal 92.307 and Coefficient of Determination was equal 0.962.

Keywords

References

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History

  • Receive Date: 03 February 2018
  • Accept Date: 03 February 2018

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