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Year 2023, Volume: 6 Issue: 2, 1 - 6, 31.12.2023

Abstract

References

  • Bai, S., Wang, J., Li, L., Xiang, Q., Wu, S. (2018). Landslide susceptibility mapping using machine learning methods: A case study from the Wanzhou District, Three Gorges Reservoir Area, China. Geomorphology, 318, 246-262.
  • Qi, S., Zhang, G., Han, Y., Zhang, H., Zhang, X. (2017). Regional landslide susceptibility assessment using GIS-based machine learning techniques: A case study of the Guizhou Province, China. Geomorphology, 285, 142-157.
  • Magliocca, N. R., Rudel, T. K., Verburg, P. H., McConnell, W. J., Mertz, O., Gerstner, K., Ellis, E. C. (2018). Synthesis in land change science: methodological patterns, challenges, and guidelines. Regional Environmental Change, 18(1), 1-13.
  • Gorum, T., Fan, X., van Westen, C. J., Huang, R. Q., Xu, Q., Tang, C. (2017). Distribution pattern of earthquake-induced landslides triggered by the 20 April 2013 Lushan earthquake of China derived from high-resolution satellite images. Landslides, 14(2), 767-779.
  • Loye, A., Jaboyedoff, M., Pedrazzini, A. (2009). Identification of potential rockfall source areas at a regional scale using a DEM-based geomorphometric analysis. Natural Hazards and Earth Systems Sciences, 9, 1643-1653.
  • Dorren, L.K.A., Seıjmonsbergen A.C. (2003). Comparison of three GIS-based models for predicting rockfall runout zones at a regional scale. Geomorphology, 56(1-2), 49-64.
  • Jaboyedoff, M., Labiouse, V. (2011). Technical note: Preliminary estimation of rockfall run out zones. Nat Hazards Erath Syst. Sci. 11, 819-828.
  • Capons, R., Vilaplana, J.M., Linares, R. (2009). Rockfall travel distance analysis by using empirical models. Natural Hazards Earth System Science. 9, 2107-2118.
  • Evans, S.G., Hungr, O. (1993). The assessment of rockfall hazard at the base of talus slopes. Canadian Geotechnical Journal. 30, 620-636.
  • Wieczorek, G.F., Morissey, M.M., Lovine, G., Godt, J. (1998). Rockfall hazards in Yosemite Valley: U.S. Geological Survey Open file Report. 98-467.

Determination of Rockfall Potential Source areas of Yeşilbaşköy Village Burdur Türkiye

Year 2023, Volume: 6 Issue: 2, 1 - 6, 31.12.2023

Abstract

The management of rockfall risk and mitigation of its effects are gaining importance as an effective strategy in the fight against disasters. Geographic Information Systems are used as a powerful tool in rockfall risk mapping and susceptibility analysis. The aim of this study is to determine the rockfall source areas of Yeşilbaşköy village in Burdur province by using Geographical Information Systems and Conefall software and to classify these areas as low-medium and high-risk areas. In the first step, a digital elevation map was produced by digitizing the 1/25000 scale topographic maps of the study area. Potential rockfall source areas were determined by using digital elevation model and slope map was created. Considering the critical slope angles, low-medium and high-risk rockfall areas were mapped in the Yeşilbaşköy region. Low-risk areas represent areas where slopes are lower and less problematic in terms of stability. On the other hand, high-risk areas indicate steep slopes, loose soils and susceptibility areas in terms of geological structure. The results of this study provide an important basis for understanding the distribution of rockfall risk in Yeşilbaşköy village and for developing disaster management strategies. Thanks to the analytical and visualization capabilities provided by Geographic Information Systems, it becomes easier to create disaster risk maps and to use them in decision-making processes. This helps local governments and decision makers to allocate resources effectively and plan risk reduction measures.

References

  • Bai, S., Wang, J., Li, L., Xiang, Q., Wu, S. (2018). Landslide susceptibility mapping using machine learning methods: A case study from the Wanzhou District, Three Gorges Reservoir Area, China. Geomorphology, 318, 246-262.
  • Qi, S., Zhang, G., Han, Y., Zhang, H., Zhang, X. (2017). Regional landslide susceptibility assessment using GIS-based machine learning techniques: A case study of the Guizhou Province, China. Geomorphology, 285, 142-157.
  • Magliocca, N. R., Rudel, T. K., Verburg, P. H., McConnell, W. J., Mertz, O., Gerstner, K., Ellis, E. C. (2018). Synthesis in land change science: methodological patterns, challenges, and guidelines. Regional Environmental Change, 18(1), 1-13.
  • Gorum, T., Fan, X., van Westen, C. J., Huang, R. Q., Xu, Q., Tang, C. (2017). Distribution pattern of earthquake-induced landslides triggered by the 20 April 2013 Lushan earthquake of China derived from high-resolution satellite images. Landslides, 14(2), 767-779.
  • Loye, A., Jaboyedoff, M., Pedrazzini, A. (2009). Identification of potential rockfall source areas at a regional scale using a DEM-based geomorphometric analysis. Natural Hazards and Earth Systems Sciences, 9, 1643-1653.
  • Dorren, L.K.A., Seıjmonsbergen A.C. (2003). Comparison of three GIS-based models for predicting rockfall runout zones at a regional scale. Geomorphology, 56(1-2), 49-64.
  • Jaboyedoff, M., Labiouse, V. (2011). Technical note: Preliminary estimation of rockfall run out zones. Nat Hazards Erath Syst. Sci. 11, 819-828.
  • Capons, R., Vilaplana, J.M., Linares, R. (2009). Rockfall travel distance analysis by using empirical models. Natural Hazards Earth System Science. 9, 2107-2118.
  • Evans, S.G., Hungr, O. (1993). The assessment of rockfall hazard at the base of talus slopes. Canadian Geotechnical Journal. 30, 620-636.
  • Wieczorek, G.F., Morissey, M.M., Lovine, G., Godt, J. (1998). Rockfall hazards in Yosemite Valley: U.S. Geological Survey Open file Report. 98-467.
There are 10 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Original Research Articles
Authors

Kerem Hepdeniz 0000-0003-4182-5570

Publication Date December 31, 2023
Acceptance Date November 29, 2023
Published in Issue Year 2023 Volume: 6 Issue: 2

Cite

APA Hepdeniz, K. (2023). Determination of Rockfall Potential Source areas of Yeşilbaşköy Village Burdur Türkiye. Scientific Journal of Mehmet Akif Ersoy University, 6(2), 1-6.