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Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey)

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  • T. Gorum
  • B. Gonencgil
  • C. Gokceoglu
  • H. Nefeslioglu

Abstract

In the international literature, although considerable amount of publications on the landslide susceptibility mapping exist, geomorphology as a conditioning factor is still used in limited number of studies. Considering this factor, the purpose of this article paper is to implement the geomorphologic parameters derived by reconstructed topography in landslide susceptibility mapping. According to the method employed in this study, terrain is generalized by the contours passed through the convex slopes of the valleys that were formed by fluvial erosion. Therefore, slope conditions before landsliding can be obtained. The reconstructed morphometric and geomorphologic units are taken into account as a conditioning parameter when assessing landslide susceptibility. Two different data, one of which is obtained from the reconstructed DEM, have been employed to produce two landslide susceptibility maps. The binary logistic regression is used to develop landslide susceptibility maps for the Melen Gorge in the Northwestern part of Turkey. Due to the high correct classification percentages and spatial effectiveness of the maps, the landslide susceptibility map comprised the reconstructed morphometric parameters exhibits a better performance than the other. Five different datasets are selected randomly to apply proper sampling strategy for training. As a consequence of the analyses, the most proper outcomes are obtained from the dataset of the reconstructed topographical parameters and geomorphologic units, and lithological variables that are implemented together. Correct classification percentage and root mean square error (RMSE) values of the validation dataset are calculated as 86.28% and 0.35, respectively. Prediction capacity of the different datasets reveal that the landslide susceptibility map obtained from the reconstructed parameters has a higher prediction capacity than the other. Moreover, the landslide susceptibility map obtained from the reconstructed parameters produces logical results. Copyright Springer Science+Business Media B.V. 2008

Suggested Citation

  • T. Gorum & B. Gonencgil & C. Gokceoglu & H. Nefeslioglu, 2008. "Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 46(3), pages 323-351, September.
    • Handle: RePEc:spr:nathaz:v:46:y:2008:i:3:p:323-351
    DOI: 10.1007/s11069-007-9190-6
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    References listed on IDEAS

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    1. Juan Remondo & Alberto González-Díez & José De Terán & Antonio Cendrero, 2003. "Landslide Susceptibility Models Utilising Spatial Data Analysis Techniques. A Case Study from the Lower Deba Valley, Guipuzcoa (Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 267-279, November.
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    Cited by:

    1. Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1231-1258, June.
    2. Hadi Memarian & Shiva Abdi Bastami & Morteza Akbari & Seyed Mohammad Tajbakhsh & Mahmoud Azamirad, 2023. "An integrative approach of the physical-based stability index mapping with the maximum entropy stochastic model for risk analysis of mass movements," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2808-2830, March.
    3. Krishna Devkota & Amar Regmi & Hamid Pourghasemi & Kohki Yoshida & Biswajeet Pradhan & In Ryu & Megh Dhital & Omar Althuwaynee, 2013. "Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 135-165, January.
    4. Vahid Nourani & Biswajeet Pradhan & Hamid Ghaffari & Seyed Sharifi, 2014. "Landslide susceptibility mapping at Zonouz Plain, Iran using genetic programming and comparison with frequency ratio, logistic regression, and artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 71(1), pages 523-547, March.
    5. Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 883-900, September.
    6. Nefeslioglu, Hakan A. & Gorum, Tolga, 2020. "The use of landslide hazard maps to determine mitigation priorities in a dam reservoir and its protection area," Land Use Policy, Elsevier, vol. 91(C).

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