Statistical Analysis Of Hydrological Drivers Of Landslide Inducing Factors Using Weights Of Evidence Approach On Kerch Peninsula

Year 2022, Volume: 9 Issue: 1, 60 - 63, 06.03.2022

Denis Krivoguz

https://doi.org/10.30897/ijegeo.767519

Cited By: 1

Abstract

Article is devoted to statistical analysis of the influence of the surface water of Kerch Peninsula on landslides. We chose a distance from water as indicator that can be express the impact of surface water on landslides. The territory of the peninsula was divided into 5 classes according to the degree of distance. Thus, according to the results of the study, it was found that the greatest impact on landslides is observed at a distance of up to 500 m. On the other hand, it is noted that there is no effect of surface water on landslides at a distance over 2 km.

Keywords

Landslides, GIS, Statistical analysis, Surface waters, Kerch peninsula

References

• Ayalew, L., Yamagishi, H., & Ugawa, N. (2004). Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides, 1(1), 73–81. https://doi.org/10.1007/s10346-003-0006-9
• Bathurst, J. C., Moretti, G., El-Hames, A., Beguería, S., & García-Ruiz, J. M. (2007). Modelling the impact of forest loss on shallow landslide sediment yield, Ijuez river catchment, Spanish Pyrenees. Hydrology and Earth System Sciences, 11(1), 569–583. https://doi.org/10.5194/hess-11-569-2007
• Dahal, R. K., & Hasegawa, S. (2008). Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology, 100(3–4), 429–443. https://doi.org/10.1016/j.geomorph.2008.01.014
• Krivoguz, D., & Bespalova, L. (2017). Spatial analysis of topography of Kerch peninsula using GIS and its impact on landslides. International Journal of Professional Science, (6), 19–32.
• Krivoguz, D., & Bespalova, L. (2018). Analysis of Kerch peninsula’s climatic parameters in scope of landslide susceptibility. Vestnik Kerchenskogo Gosudarstvennogo Morskogo Tehnologicheskogo Universiteta, (2), 5–11.
• Krivoguz, D. O., & Burtnik, D. N. (2018). Neural network modeling of changes in the land cover of the Kerch peninsula in the context of landslides occurence. Nauchno-Tekhnicheskiy Vestnik Bryanskogo Gosudarstvennogo Universiteta, 4(1), 113–121. https://doi.org/10.22281/2413-9920-2018-04-01-113-121
• Krivoguz, Denis. (2020). Methodology of physiography zoning using machine learning: A case study of the Black Sea. Russian Journal of Earth Sciences, 20(1), 1–10. https://doi.org/10.2205/2020ES000707
• Krivoguz, Denis, & Bespalova, L. (2020). Landslide susceptibility analysis for the Kerch Peninsula using weights of evidence approach and GIS. Russian Journal of Earth Sciences, 20(1), 1–12. https://doi.org/10.2205/2020ES000682
• Lari, S., Frattini, P., & Crosta, G. B. (2014). A probabilistic approach for landslide hazard analysis. Engineering Geology, 182, 3–14. https://doi.org/10.1016/j.enggeo.2014.07.015
• Margielewski, W., & Urban, J. (2000). The type of initiation of mass movements in the Flysch Carpathians studied on the base of structural development of the selected crevice type caves (southern Poland). Przeglad Geologiczny, 48(3).
• Van Westen, C. (1997). Statistical landslide hazard analysis. Ilwis, 2, 1–10. Retrieved from http://www.adpc.net/casita/Case_studies/Landslide hazard assessment/Statistical landslide susceptibility assessmen landslide index method CS Chinchina Colombia/Statistical_landslide_susceptibility_analysis.pdf
• van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3–4), 112–131. https://doi.org/10.1016/j.enggeo.2008.03.010