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Landslide Hazard Mapping Using the Artificial Neural Network a Part of Haraz Watershed | ||
| پژوهش های آبخیزداری | ||
| Article 2, Volume 29, Issue 4 - Serial Number 113, January 2017, Pages 9-19 PDF (914.42 K) | ||
| Document Type: Research | ||
| DOI: 10.22092/wmej.2017.115313 | ||
| Authors | ||
| Alireza Sepah Vand* 1; Hamid Reza Moradi2; Parviz Abdolmaleki3 | ||
| 1Department of Range and Watershed Management Engineering, faculty of Agriculture and Natural Resources, Lorestan University | ||
| 2Associate Prof., Faculty of Natural Resources, Tarbiat Modares University | ||
| 3Associate 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 | ||
| Landslide; Artificial Neural Network; Back Propagation Algorithm; Sigmoid Function | ||
| References | ||
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Alijani B., Ghohrodi M. and Ahmadi A.A., 2007: Landslide susceptibility zonation using GIS in north hillslope of Shahjehan, Geographics Research, No, 84. PP: 116-131.
Banimahd M. and Yasrebi S. 2003. Application artificial neural networks in geotechnical engineering: modeling, analysis, design, Engineering Modares Journal, Vol, 14. PP: 1-13.
Caniani D., Pascale S., Sdao F. and Sole A. 2008. Neural networks and landslide susceptibility: a case study of the urban area of Potenza, Natural Hazards, Vol, 45. PP: 55–72.
Ermini L., Catani F. and Casagli N., 2005: Artificial neural network to landslide susceptibility assessment, Geomorphology, Vol, 66. PP: 327–343.
Esmaeili Vareki M., Omid M. and Omid M.H. 2005. Estimate diverging hydraulic jump profile using artificial neural networks, Agriculture and Natural Resources Sciences, Vol, 12. PP: 21-32.
Gomez H. and Kavzoglu T., 2005: Assessment of shallow landslide susceptibility using artificial lneural networks in Jabonosa River Basin, Venezuela, Engineering Geology, Vol, 78, No, 1–2. PP: 11–27.
Lee S., Ryu J. H., Lee M. J. and Won J. S., 2003: Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea, Environmental Geology, Vol, 44. PP: 820–833.
Lee S., Ryu J. H., Won J. S. and Park H. J., 2004: Determination and application of the weights for landslide susceptibility mapping using an artificial neural network, Engineering Geology, Vol, 71. PP: 289–302.
Lee S., Ryu J. H., Lee M. J. and Won J. S., 2006: The Application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea, Mathematical Geology, Vol, 38, No, 2. PP: 199-220.
Lin W., Chou W. and Lin C., 2008: Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan, Natural Hazards, Vol, 47. PP: 331–347.
Melchiorre C., Matteucci M. and Azzoni A., 2008: Artificial neural networks and cluster analysis in landslide susceptibility zonation, Geomorphology, Vol, 94. PP: 379 – 400.
Mohammadi M., Moradi H. R., Feiznia, S. and Pourghasemi H. R. 2008. Effects of Rangeland vegetation on slope stability in a part of Haraz Watershed Using GIS. Rangeland Journal, Vol, 2, No, 3. PP: 289-300.
Moghimi A., Elavipanah S.K. and Jafari T. 2008: Assessment and effects parameters zonation in occurrence landslides Aladagh, Geographic Researches, Vol, 64. PP: 53-75.
Pourghasemi H. R., Moradi H. R., Mohammadi M., and Mahdavifar M. R. 2008. Landslide Hazard Assessment and Evaluation Using Fuzzy Operators, Agriculture and Natural Resources Sciences, Vol, 12, No, 46. PP: 375-390.
Rakei B., Khamechian M., Abdolmaleki P. and Giahchi P. 2007. Application artificial neural networks in Landslide zonation, Sciences Tehran University, Vol, 33, No, 1. PP: 57-64.
Shadfar, S., Yamani, M., Ghodosi J. and Ghayomian. 2007. Landslide hazard zonation using AHP method, Pajouhesh and sazandegi, Vol, 75. PP: 118-126.
Shirani K., Chavoshi S. and Ghauomian J., 2006: Assessment and evaluation landslide susceptibility zonation in Padena Semirom, Science Research Esfahan university, Vol, 23, No, 1. PP: 23-38.
Yesilnacar E. and Topal T., 2005: Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey), Engineering Geology, Vol, 79. PP: 251–266.
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