Volume 6, Issue 4 (2018)                   IQBQ 2018, 6(4): 259-268 | Back to browse issues page

XML Print

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

Zarei P, Talebi A, Alaie Taleghani M. Sensitivity Analysis of Effective Factors in Hillslopes Instability; A Case Study of Javanrud Region, Kermanshah Province . IQBQ. 2018; 6 (4) :259-268
URL: http://journals.modares.ac.ir/article-24-23269-en.html
1- Geography Department, Geography Faculty, Razi University of Kermanshah, Kermanshah, Iran
2- Watershed Management Department, Natural Resources Faculty, Yazd University, Yazd, Iran , talebisf@yazd.ac.ir
Abstract:   (86 Views)
Aims: Evaluating the factors affecting the mass movement and recognizing the regions sensitive to landslide are vital for planning, performing the construction projects, and providing proper management solutions in sensitive regions. The aim of the present study was to investigate the stability of the hillslope using the Stability Index Mapping (SINMAP) model to recognize the most important factor in causing the landslide by one-time sensitivity analysis method.
Materials & Methods: In the experimental research, the studied area included several watersheds in Javanrud, Kermanshah Province, Iran. Sensitivity analysis was performed for slope angle, internal friction angle, depth of soil, hydraulic conductivity, saturated storage ratio and rainfall. Accordingly, each of the mentioned parameters was changed by 10% to 75% compared to their initial value, assuming that other parameters remain constant. Then, the safety factor (FS) for each variation and the ratio of safety factor variations to initial FS were calculated.
Findings: The slope angle was the most important effective factor in causing the landslide in this region. The Second and the third factors were internal friction angle and saturated storage ratio, respectively.
Conclusion: The slope angle is the most important factor in causing the instability in all hillslopes, as where this factor is reduced by 20%, FS initial value increased by twice. After slope angle, soil internal friction angle has the highest importance, which shows a direct relationship with factor of safety. It means that, as this angle increase, stability of the hillslopes will also increase.

Full-Text [PDF 1182 kb]   (29 Downloads)    

Received: 2018/07/20 | Accepted: 2018/09/15 | Published: 2018/11/21
* Corresponding Author Address: Natural Resources Faculty, Yazd University, Safaieh, Yazd, Iran

1. Ilia l, Tsangaratos P. Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides. 2015;13(2):379-97. [Link]
2. Schilirò L, Esposito C, Scarascia Mugnozza G. Evaluation of shallow landslide-triggering scenarios through a physically based approach: An example of application in the Southern Messina area (Northeastern Sicily, Italy). Nat Hazards Earth Syst Sci. 2015;15:2091-109. [Link] [DOI:10.5194/nhess-15-2091-2015]
3. Ho JY, Lee KT. Performance evaluation of a physically based model for shallow landslide prediction. Landslides .2017;14(3):961-80. [Link] [DOI:10.1007/s10346-016-0762-y]
4. Montgomery DR, Dietrich WE. A physically based model for the topographic control on shallow landsliding. Water Resour Res. 1994;30(4):1153-71. [Link] [DOI:10.1029/93WR02979]
5. Wu W, Sidle RC. A distributed slope stability model for steep forested basins. Water Resour Res. 1995;31(8):2097-110. [Link] [DOI:10.1029/95WR01136]
6. Talebi A, Uijlenhoet R, Troch PA. A low-dimensional physically based model of hydrologic control of shallow landsliding in complex hillslopes. Earth Surf Process Landf. 2008;33(13):1964-76. [Link] [DOI:10.1002/esp.1648]
7. Borga M, Dalla Fontana G, Gregoretti C, Marchi L. Assessment of shallow landsliding by using a physically based model of hillslope stability. Hydrol Process. 2002;16(14):2833-51. [Link] [DOI:10.1002/hyp.1074]
8. Talebi A, Troch PA, Uijlenhoet R. A steady‐state analytical slope stability model for complex hillslopes. Hydrol Process .2008;22(4):546-53. [Link] [DOI:10.1002/hyp.6881]
9. Claessens L, Knapen A, Kitutu MG, Poesen J, Deckers JA. Modelling landslide hazard, soil redistribution and sediment yield of landslides on the Ugandan footslopes of Mount Elgon. Geomorphology. 2007;90(1-2):23-35. [Link] [DOI:10.1016/j.geomorph.2007.01.007]
10. Wawer R, Nowocień E. Application of SINMAP terrain stability model to Grodarz stream watershed. Electron J Pol Agric Univ. 2003;6(1):03. [Link]
11. Terhorst B, Kreja R. Slope stability modelling with SINMAP in a settlement area of the Swabian Alb. Landslides. 2009;6(4):309-19. [Link] [DOI:10.1007/s10346-009-0167-2]
12. Michel GP, Kobiyama M, Goerl RF. Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil. J Soils Sediments. 2014;14(7):1266-77. [Link] [DOI:10.1007/s11368-014-0886-4]
13. Nery TD, Vieira BC. Susceptibility to shallow landslides in a drainage basin in the Serra do Mar, São Paulo, Brazil, predicted using the SINMAP mathematical model. Bull Eng Geol Environ. 2015;74(2):369-78. [Link] [DOI:10.1007/s10064-014-0622-8]
14. Lazzari M, Gioia D. Regional-scale landslide inventory, central-western sector of the Basilicata region (Southern Apennines, Italy). J Maps. 2016;12(5):852-9. [Link] [DOI:10.1080/17445647.2015.1091749]
15. Virajh Dias AA, Gunatilake J. Assessing shallow landslide susceptibility by deterministic model approach SINMAP. J Geol Soc Sri Lanka. 2017;18(2):17-31. [Link]
16. Akgun A, Erkan O. Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: In an artificial reservoir area at Northern Turkey. Arab J Geosci. 2016;9:165. [Link] [DOI:10.1007/s12517-015-2142-7]
17. Faria A, Bateira C, Laura S, Fernandes J, Gonçalves J, Marques F. Landslide susceptibility evaluation on agricultural terraces of Douro Valley (Portugal), using physically based mathematical models. EGU Gen Assem. 2016;18:17801. [Link]
18. Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, et al. Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ Model Softw. 2016;79:214-32. [Link] [DOI:10.1016/j.envsoft.2016.02.008]
19. Hamm NAS, Hall JW, Anderson MG .Variance-based sensitivity analysis of the probability of hydrologically induced slope instability. Comput Geosci. 2006;32(6):803-17. [Link] [DOI:10.1016/j.cageo.2005.10.007]
20. Bathurst JC, Moretti G, El-Hames A, Moaven-Hashemi A, Burton A. Scenario modelling of basin-scale, shallow landslide sediment yield, Valsassina, Italian Southern Alps. Nat Hazards Earth Syst Sci. 2005;5:189-202. [Link] [DOI:10.5194/nhess-5-189-2005]
21. Hennrich K, Crozier MJ. A hillslope hydrology approach for catchment-scale slope stability analysis. Earth Surf Process Landf. 2004;29(5):599-610. [Link] [DOI:10.1002/esp.1054]
22. Wagener T, Kollat J. Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox. Environ Model Softw. 2007;22(7):1021-33. [Link] [DOI:10.1016/j.envsoft.2006.06.017]
23. Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S. Variance based sensitivity analysis of model output, design and estimator for the total sensitivity index. Comput Phys Commun. 2010;181(2):259-70. [Link] [DOI:10.1016/j.cpc.2009.09.018]
24. Marrel A, Iooss B, Da Veiga S, Ribatet M. Global sensitivity analysis of stochastic computer models with joint metamodels. Stat Comput. 2012;22(3):833-47. [Link] [DOI:10.1007/s11222-011-9274-8]
25. Dobler C, Pappenberger F. Global sensitivity analyses for a complex hydrological model applied in an Alpine watershed. Hydrol Process. 2013;27(26):3922-40. [Link] [DOI:10.1002/hyp.9520]
26. Tang Y, Reed P, Wagener T, van Werkhoven K. Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation. Hydrol Earth Syst Sci. 2007;11:793-817. [Link] [DOI:10.5194/hess-11-793-2007]
27. Gioia E, Speranza G, Ferretti M, Godt JW, Baum RL, Marincioni F. Application of a process-based shallow landslide hazard model over a broad area in Central Italy. Landslides. 2016;13(5):1197-214. [Link] [DOI:10.1007/s10346-015-0670-6]
28. Alaee Taleghani M, Rahimzadeh Z. Simulation of landslide risk in Javanroud basin using AHP method considering geomorphic properties. Geogr Environ Plann J. 2012;22(4):53-72. [Persian] [Link]
29. Kayastha P. Slope stability analysis using GIS on a regional scale. J Nepal Geo Soc. 2007;36:19 [Link]
30. Nasiri M, Hosseini SA. Effect of LS factor on soil loss rate from cut slopes after the construction of forest roads. J Environ Sci Manag. 2012;15(2):13-8. [Link]
31. Nafarzadegan AR, Talebi A, Malekinezhad H, Emami N. Antecedent rainfall thresholds for the triggering of deep-seated landslides (Case study: Chaharmahal & Bakhtiari province, Iran). Ecopersia. 2013;1(1):23-39. [Link]

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

Send email to the article author