Investigation of relationship between sediment yield and landslide in Iran

Abstract

Landslides have been made irreversible damage to urban areas and economic in Iran. In this research, at first, for Investigation of relationship between landslide and sediment yield was recognized some of effective factors on Landslide. These Factors were processed with use of ILWIS and Arc GIS software’s. Landslide hazard zonation was done using Density Area and Index Overlay methods in GIS and evaluated them using Quality Sum index. In after phase, were determined sediment yield in each of them. Finally, occurrence rate landslide investigated in sediment yield zones. The results indicated that, slope, lithology and distance from the hydrographic network have the greatest impact on landslides. Most of the landslides have occurred in the 15-40% slope class, units of conglomerate and marl, and within one km of drainage network. On the other hand, the relationship between landslide frequency and distance of the fault was not a linear relationship and Almost 60 %of landslides have occurred distance of one km of the faults. Evaluation using Quality Sum index showed that the density Area has a more logical answer and as Appropriate method will be introduced in the basin. Investigation of deposition potential in sub-basins showed that Javaherdeh sub basin with 92.74 deposition potential is the first priority. Nedasht and latmohalleh sub basins, each with a deposition potential of 20.08 are the next priorities. Relationship between landslide area and deposition potential were identified as 8/91% of the landslides in the area of low And about 79 percent of landslides are located in high and very high deposition potentials.  

Keywords

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References

1. Aleotti, P., Chowdhury, R., 1999. Landslide hazard assessment, summary review and new perspectives. Bulletin of Engineering Geology and the Environment 58, 21-44.
2. Celerici, A. , Prego, S., Tellini, C., Vescovi, P., 2002. A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48, 349-364.
3. Donati. L., Turrini, M.C., 2002. An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology: application to an area of the Apennines (Valnerina; Perugia, Italy). Engineering Geology 63, 277-289.
4. Mandy, L.G., Andrew, M.W., Richard, A. Stephan, G.C., 2001. Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology 37, 149-165.
5. Nandi, A., Shakoor, A., 2010. A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses Engineering Geology 110, 11-20
6. Nikandish,N, 2000. Impact of landslides in sediment yield Karoon watershed, Proceedings of the Second National Conference, University of Lorestan, pp134-117
7. Pradhan, B., 2010. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research 45, 1244-1256.
8. Schuster, R.L., 1996. Socioeconomic significance of landslides, In: Turner, A.K. and Schuster, R.L. (Eds), Landslides: Investigation and mitigation special Report, vol, 247, National Academic Press, Washington, DC, pp:12-36.

9. Shadfar, S, 2005, Evaluation of landslide models in order to achieve suitable model in chalk rood watershed, P.H.DThesis, PP: 225.
10. van Western, C.J, Castellanosa, E., Kuriakose, S.L., 2008. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102, 112-131
11. Yilmaz, I., 2009. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Computers & Geosciences 35, 1125-1138