Landslide susceptibility mapping using precipitation data, Mazandaran Province, north of Iran

Mohammad Arab Amiri () and Christian Conoscenti ()
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Mohammad Arab Amiri: K. N. Toosi University of Technology
Christian Conoscenti: University of Palermo

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 89, issue 1, No 12, 255-273

Abstract: Abstract Precipitation is a nonlinear and complex phenomenon and varies in time and space. It is also evident that there is a link between precipitation and shallow landslides, and precipitation is always considered as a landslide-triggering factor. This study aims to investigate the relationship between the characteristics of precipitation and the historical shallow landslides in Mazandaran Province, north of Iran. For this purpose, the spatial variability of rainfall was analyzed using monthly rainfall data collected at 15 synoptic stations distributed over the region between 1981 and 2014. Monthly precipitation and other derived parameters were used, and a hybrid model combining principal component analysis and cluster analysis (CA) was applied to all the precipitation parameters to regionalize the region into well-defined clusters in terms of precipitation and prove that there is a link between precipitation and the occurred slides. Then, the rotated PCs were combined and the precipitation characteristics map was produced. Demonstrating the linkage between the precipitation characteristics and the historical slides, the combined map can be considered as landslide susceptibility map. The accuracy of prediction was tested against a random guess and obtained as 77%. It is also noticeable that only 30% of the surface area of the study region in the landslide susceptibility map covers about 80% of the known landslides. The calculated measure suggests that the developed model well predicted the location of the occurred slides using only precipitation data.

Keywords: Cluster analysis; Landslides; Mazandaran Province; Principal component analysis; Precipitation (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11069-017-2962-8

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