Volume 10, Issue 19 (5-2019)
jwmr 2019, 10(19): 117-131 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
eslami M, shadfar S, mohamadi torkashvand A, pazira E. (2019). Application of Artificial Neural Network in Study Phenomenon of Landslide and Risk Modeling using Geographic Information System (GIS), Case Study: Alamoot Rood Watershed. jwmr. 10(19), 117-131. doi:10.29252/jwmr.10.19.117
URL: http://jwmr.sanru.ac.ir/article-1-891-en.html
URL: http://jwmr.sanru.ac.ir/article-1-891-en.html
Soil Conservation and Watershed Management Research Institute (SCWMRI), Agricultural Research, Education and Extension Organization (AREEO), Tehran
Abstract: (3368 Views)
One of the natural disasters that occurs in abundance in Iran, due to the geological structure, morphological and seismic conditions, and damages the lives and property of people is a landslide. Roodbar Alamoot watershed in the east of Qazvin province is a mountainous region with a high potential for occurrence of landslides. Because of their active status, there is also a growing trend of landslide occurrence and damage to rangeland, agricultural lands and residential areas. In this research, landslide survey was conducted using Artificial Neural Network model (ANN). Soil, geology, slope, aspect, elevation classes, linear parameters including distance from the river, distance from the fault, distance from the road, sensitivity of the rocks to erosion, rainfall and land use as factors affecting landslide. Using artificial neural network model with the multiple-layer perceptron structure and back propagation learning algorithm, landslide hazard zonation was performed. The results showed that the arrangement of 11-7-1 with active sigmoid function is the best structure for studying the phenomenon of landslide in this study area. The training, test and validation of the model were performed with 15, 15 and 75 Percentage of data that randomly selected. After optimizing the network structure, standardized information was provided to the network. Based on the results of landslide hazard zonatin with Artificial Neural Network model, respectively, 6.2, 10.7, 17.1, 64.3 and 5.3 percent of the area placed in the very low, low, moderate, high and very high risk classes. The network has 0.5 learning ratio, 7 neurons in the hidden layer and the least amount of error in the experiment (RMSe = 0.0321)
Keywords: ANN, Back Propagation Learning Algorithm, Land Use, Linear Parameters, Perceptron Structure
Type of Study: Research |
Subject:
سنجش از دور و سامانه های اطلاعات جغرافيايی
Received: 2017/12/24 | Revised: 2019/07/31 | Accepted: 2018/07/25 | Published: 2019/08/3
Received: 2017/12/24 | Revised: 2019/07/31 | Accepted: 2018/07/25 | Published: 2019/08/3
References
1. 1. Abedini, M. and H. Setayeshi. 2014. Landslide hazard zonation with hierarchical analysis model (case study: Golgeh watershed). Geography and Planning, 49: 139-165 (In Persian).
2. Amirahmadi, A., M. Mohammadnia and L. Solgi. 2015. Preparation of landslide sensitivity map using frequency ratio and analytical hierarchy process compilation model. Applied Geomorphology of Iran, 5: 45-58 (In Persian).
3. Crosta, B.G. 2009. Dating, triggering, modeling and hazard assessment of large landslides. Geomorphology, 103: 1-4. [DOI:10.1016/j.geomorph.2008.04.007]
4. Emami, S. N., A. Jalalian and A. Khosravi. 2016. The Role of Soil Chemical and Physical Characteristics in Landslide Occurrence (Case Study: Afsar Abad Area in Chaharmahal and Bakhtiari Province), Journal of Watershed Management Research, 7(13): 182-192 (In Persian). [DOI:10.18869/acadpub.jwmr.7.13.192]
5. Forests, Range and Watershed Management Organization. 1996. Combined report of erosion and sedimentation of Alamoutrood basin, 87 pp (In Persian).
6. Forests, Range and Watershed Management Organization. 2010. Jutan Watershed Executive Studies, Geology, Geomorphology, Pedology, Land Capability and Vegetation, 196 pp (In Persian).
7. Forests, Range and Watershed Management Organization .2016. Iran Landslides Database Report, 135 pp (In Persian).
8. Ghahramani, S. and M.R. Servati. 2009. Study of geomorphology and erosion in Alamout River basin. Land Geographic Quarterly, 45-61 (In Persian).
9. Golabi, M.R., A.M. Akhondali and F. Radmanesh. 2013. Comparing the performance of different artificial neural networks algorithms in modeling the rainy season, case study: selected stations in Khozestan province. Journal of Geographical Sciences and Applied Research, 30: 151-169 (In Persian).
10. Gomez, H., T. Kavzoglu and P. Mather. 2002. Artificial neural networks in landslide hazard zonation in the Venezuelan Andes, Abstracts of 15th International Conference on Geomorphology, Geomorph. Uni, 22(4): C-76.
11. Guzzetti, F. 2005. Landslide hazard and risk assessment. Dissertation. Anungo, D.P. Arora, M.K. Gupta, R.P. Sarkar, S. 2008. Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Malayas.Landslides, 5: 407-416. [DOI:10.1007/s10346-008-0134-3]
12. Hasanzadeh, M.H., M. Chabok and Z. Ebrahimi. 2012. Landslide hazard zonation using SMCE model, Case study: shalmanrood basin. Water and soil conservation, 19(1): 99-116 (In Persian).
13. Hejazi, S.A. 2014. Landslide hazard zonation in Ahar city, goyjabel Basin using geographic information system (GIS). Geography and Planning Journal, 50: 135-152 (In Persian).
14. Lan, H.X., C.H. Zhou, L.J. Wang, H.Y. Zhang and R.H. Li. 2004. Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang Watershed, Yunnan, China. Engineering Geology, 76: 109-128. [DOI:10.1016/j.enggeo.2004.06.009]
15. Menhaj, M.B. 2002. Fonundations of neural networks. Amirkabir University Press, 715 pp (In Persian).
16. Misaghi, F. 2002. Simulation of rainfall-runoff for river rotating using artificial neural networks. M. Sc., Tarbiat Modares University, 46 pp (In Persian).
17. Moghimi, E., S. Bagheri and T. Safarzadeh. 2012. Landslide hazard zonation using entropy model, Case study: Northwest Zagros. Journal of Natural Geography Research, 79: 77-90 (In Persian).
18. Mohammadi, M., H.R. Moradi, S. Feiznia and H.R. Pourghasemi. 2009. Prioritize the factors affecting the landslide and prepared a risk map using the information value and AHP models, Haraz watershed. Earth Science, 74: 27-32 (In Persian).
19. Moradi, H.R., A.R. Sepahvand and P. Abdolmaleki. 2012. Investigating the effect of number of input factors on the accuracy value of artificial neural network for landslide hazard zonation: Case study: Haraz watershed. Pasture and Watershed Management, 65(2): 231-243 (In Persian).
20. Mostafaei, J. and M. Ownegh. 2011. Assessment and prediction of landslide risk using regression model and analytical hierarchy process, Case study: Alamoot Basin. Engineering and Watershed Management, 3(3): 149-159 (In Persian).
21. Nilsen, T.H., F.H. Wright, C. Vlasic and W. Spangle. 1979. Relative slope stability and landuse planning in the San Francisco Bay region. California: U.S Geological Survey Professional. [DOI:10.3133/pp944]
22. Raghuvanshi, T.K., L. Negassa and P.M. Kala. 2015. GIS based grid overlay method versus modeling approach-a comparative study for landslide hazard zonation (LHZ) in Meta Robi District of West Showa Zone in Ethiopia. The Egyptian Journal of Remote Sensing and Space Sciences, 18: 235-250. [DOI:10.1016/j.ejrs.2015.08.001]
23. Rajabi, M. and M. Feizolahpoor. 2014. Landslide hazard zonation of Givchay River basin using multi-layer perceptron model of back-propagation type. Geography and Development, 36: 161-180 (In Persian).
24. Sadoogh, H., A.R. Azimpoor, A. Dallaloghlo and M.R. Servati. 2009. Evaluation of AHP Model in Landslide hazard zonation, (Case Study: Ahar Chay Basin). Geographic Space Magazine, 9(26): 71-87 (In Persian).
25. Safavi, S.M. 1997. Assessment of the landslide hazard in the Damavand Basin. M.Sc. Thesis, IITC, Enschede, the Netherland (In Persian).
26. Salman-Mahini, A. and S. Abedian. 2013. Landslide hazard zonation using the potential risk index for environmental planning. Environmental Management and Planning, 8(2): 5-16 (In Persian).
27. Shariat Jafari, M. 2006. Foundations and principal of sustainability natural systems. Tehran Sazeh, Tehran. Iran (In Persian).
28. Shirani, C. and A. Saif. 2012. Landslide hazard zonation using statistical methods in Pishkouh area, Fereydon-shahr. Earth Sciences, 85: 149-158 (In Persian).
29. Souri, S., G.R. Lashkaripoor, M. Ghafori and T. Farhadian Nezhad. 2011. Landslide hazard zonation using artificial neural network model in Nogian basin. Geological Engineering, 5(2): 1269-1286.
30. Souri, S., S. Baharvand and T. Farhadian Nezhad. 2013. Landslide hazard zonation using fuzzy logic, Case study: Cham Sangar basin. Journal of RS and GIS Natural Resources, 4(4): 47-60 (In Persian).
31. Tayeba, A.R., M. Dadashi, S.F. Noorbakhsh, A.A. Jamali and A. Hasanabadi. 2015. Landslide hazard zonation using the Multi-criteria Land scale assessment (SMCE) Case study: Chahar-Mahal and Bakhtiari Province Bonn Basin. Geography and Environmental Planning, 26(1): 105-116 (In Persian).
32. Vahidnia, M.H., A. Alesheikh, A. Alimohammadi and F. Hosseini. 2009. Landslide hazard zonation Using Quantitive Methods in GIS. International Journal of Civil Engineering, 7(3): 176-189 (In Persian).
33. Varnes, D.J. 1984. Landslide Hazard Zonation: A Riview of Principles and Practice, Unaited Nations Educationnal, Scientific and Cultural Organization (UNESCO), France.
34. Xilin, L., S. Wang and X. Zhang. 1992. Influence of geologic factors on landslide in zhaotong, Yunnan province, China. Environmental Geology and Water Sciences, 19: 17-20. [DOI:10.1007/BF01740573]
35. Yamani, M., A.A. Shamsipoor, A. Goorabi and M. Rahmati. 2014. Determine the boundary of landslide hazard zonation in the path Khorram Abad-PaulZal freeway with analytical hierarchy process-Fuzzy Method. Journal of Applied Geosciences Research, 32: 27-44 (In Persian).
36. Zare, M., A.R. Moghaddamnia, S. Tali Khoshk and H. Salmani. 2015. Landslide Hazard Assessment by using Neuro-Fuzzy Technique in VazWatershed. Journal of Watershed Management Research, 6(11): 101-110 (In Persian).
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
