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A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran

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  • Ataollah Shirzadi
  • Lee Saro
  • Oh Hyun Joo
  • Kamran Chapi

Abstract

This study describes the application of logistic regression to rock-fall susceptibility mapping along 11 km of a mountainous road on the Salavat Abad saddle, in southwest Kurdistan, Iran. To determine the factors influencing rock-falls, data layers of slope degree, slope aspect, slope curvature, elevation, distance to road, distance to fault, lithology, and land use were analyzed by logistic regression analysis. The results are shown as rock-fall susceptibility maps. A spatial database, which included 68 sites (34 rock-fall point cells with value of 1 and 34 no rock-fall point cells with value of 0) was developed and analyzed using a Geographic Information System, GIS. The results are shown as four classes of rock-fall susceptibility. In this study, distance to fault, lithology, slope curvature, slope degree, and distance to road were found to be the most important factors affecting rock-fall. It was concluded that about 76 % of the study area can be classified as having moderate and high susceptibility classes. Rock-fall point cells were used to verify results of the rock-fall susceptibility map using success curve rate and the area under the curve. The verification results showed that the area under the curve for rock-fall susceptibility map is 77.57 %. The results from this study demonstrated that the use of a logistic regression model within a GIS framework is useful and suitable for rock-fall susceptibility mapping. The rock-fall susceptibility map can be used to reduce susceptibility associated with rock-fall. Copyright The Author(s) 2012

Suggested Citation

  • Ataollah Shirzadi & Lee Saro & Oh Hyun Joo & Kamran Chapi, 2012. "A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran," 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. 64(2), pages 1639-1656, November.
    • Handle: RePEc:spr:nathaz:v:64:y:2012:i:2:p:1639-1656
    DOI: 10.1007/s11069-012-0321-3
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    References listed on IDEAS

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    1. Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," 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 451-472, November.
    2. Jordi Corominas & Núria Santacana, 2003. "Stability analysis of the Vallcebre translational slide, Eastern Pyrenees (Spain) by means of a GIS," 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 473-485, November.
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    1. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
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    4. Yufeng He & Mingtao Ding & Hao Zheng & Zemin Gao & Tao Huang & Yu Duan & Xingjie Cui & Siyuan Luo, 2023. "Integrating development inhomogeneity into geological disasters risk assessment framework in mountainous areas: a case study in Lushan–Baoxing counties, Southwestern 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. 117(3), pages 3203-3229, July.
    5. Youssef El Miloudi & Younes El Kharim & Ali Bounab & Rachid El Hamdouni, 2024. "Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco)," Land, MDPI, vol. 13(2), pages 1-16, February.
    6. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    7. Francisco Parra & Jaime González & Max Chacón & Mauricio Marín, 2023. "Modeling and Evaluation of the Susceptibility to Landslide Events Using Machine Learning Algorithms in the Province of Chañaral, Atacama Region, Chile," Sustainability, MDPI, vol. 15(24), pages 1-31, December.
    8. Roberta Plangg Riegel & Darlan Daniel Alves & Bruna Caroline Schmidt & Guilherme Garcia Oliveira & Claus Haetinger & Daniela Montanari Migliavacca Osório & Marco Antônio Siqueira Rodrigues & Daniela M, 2020. "Assessment of susceptibility to landslides through geographic information systems and the logistic regression model," 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. 103(1), pages 497-511, August.
    9. Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    10. Binh Thai Pham & Indra Prakash & Wei Chen & Hai-Bang Ly & Lanh Si Ho & Ebrahim Omidvar & Van Phong Tran & Dieu Tien Bui, 2019. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 11(22), pages 1-30, November.
    11. Qiang Liu & Delong Huang & Aiping Tang & Xiaosheng Han, 2021. "Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics," 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. 108(1), pages 1047-1067, August.
    12. Liu, Qiang & Huang, Delong & Zhang, Bin & Tang, Aiping & Xu, Xiuchen, 2024. "Developing a probability-based technique to improve the measurement of landslide vulnerability on regional roads," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    13. Alaa M. Al-Abadi & Noor A. Al-Najar, 2020. "Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness," 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. 100(2), pages 461-491, January.

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