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Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data

Author

Listed:
  • Cahio Guimarães Seabra Eiras

    (Federal University of Ouro Preto)

  • Juliana Ribeiro Gonçalves de Souza

    (Federal University of Ouro Preto)

  • Renata Delicio Andrade de Freitas

    (Federal University of Ouro Preto)

  • César Falcão Barella

    (Federal University of Ouro Preto)

  • Tiago Martins Pereira

    (Federal University of Ouro Preto)

Abstract

The city of Ouro Preto, which is located in the state of Minas Gerais, Brazil, has a long history of mass movements influenced by the regional geology, geomorphology, and anthropic activities, which have resulted in harmful consequences to the population. However, most of the studies conducted in the region are qualitative and are directly dependent on the experience specialists. The aim of this study was to analyse the landslide susceptibility in the urban region of Ouro Preto quantitatively by using discriminant analysis. The landslide inventory was obtained by using unmanned aerial vehicle images and fieldwork. ArcGIS 10.6 and R 3.5.1 software were used, and the following landslide predisposing factors were considered: slope angle, slope aspect, profile curvature, and topographic wetness index (TWI). As geological and geotechnical data are still scarce in the interior of Brazil, we only used data derived from topography to determine the effectiveness of these factors for analysing landslide susceptibility. The slope angle proved to be the factor that most differentiated unstable from stable terrain, followed by TWI. The other parameters were not as effective in differentiating the stability conditions. The model efficiency was 88.6%, the specificity was 93.3%, and the sensitivity was 85.0%. Also, the prediction and success curve were used to evaluate the accuracy of the proposed landslides model, by using the area under the curve (AUC) criteria. It was shown that the AUC values 0.851 for testing and 0.838 for training indicate that the developed model provides an excellent prediction. The main contribution of this work is the demonstration of the effectiveness of using easily accessible data (derived from topography) for analysing landslide susceptibility with a multivariate statistical method. This method can contribute valuable information to urban planning efforts in cities without the need for robust data.

Suggested Citation

  • Cahio Guimarães Seabra Eiras & Juliana Ribeiro Gonçalves de Souza & Renata Delicio Andrade de Freitas & César Falcão Barella & Tiago Martins Pereira, 2021. "Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data," 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. 107(2), pages 1427-1442, June.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:2:d:10.1007_s11069-021-04638-4
    DOI: 10.1007/s11069-021-04638-4
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    References listed on IDEAS

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    1. Prabin Kayastha & Megh Dhital & Florimond Smedt, 2012. "Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal," 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. 63(2), pages 479-498, September.
    2. Kourosh Shirani & Mehrdad Pasandi & Alireza Arabameri, 2018. "Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern 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. 93(3), pages 1379-1418, September.
    3. George Gaprindashvili & Cees Westen, 2016. "Generation of a national landslide hazard and risk map for the country of Georgia," 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. 80(1), pages 69-101, January.
    4. Danang Sri Hadmoko & Franck Lavigne & Guruh Samodra, 2017. "Application of a semiquantitative and GIS-based statistical model to landslide susceptibility zonation in Kayangan Catchment, Java, Indonesia," 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. 87(1), pages 437-468, May.
    5. Dimitrios Myronidis & Charalambos Papageorgiou & Stavros Theophanous, 2016. "Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)," 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. 81(1), pages 245-263, March.
    6. Haoyuan Hong & Himan Shahabi & Ataollah Shirzadi & Wei Chen & Kamran Chapi & Baharin Bin Ahmad & Majid Shadman Roodposhti & Arastoo Yari Hesar & Yingying Tian & Dieu Tien Bui, 2019. "Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods," 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. 96(1), pages 173-212, March.
    7. Christos Polykretis & Christos Chalkias, 2018. "Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, 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. 93(1), pages 249-274, August.
    8. Dimitrios Myronidis & Charalambos Papageorgiou & Stavros Theophanous, 2016. "Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP)," 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. 81(1), pages 245-263, March.
    9. 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.
    10. Zhaohua Chen & Jinfei Wang, 2007. "Landslide hazard mapping using logistic regression model in Mackenzie Valley, Canada," 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. 42(1), pages 75-89, July.
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    2. Michele Placido Antonio Gatto & Salvatore Misiano & Lorella Montrasio, 2022. "On the Use of MATLAB to Import and Manipulate Geographic Data: A Tool for Landslide Susceptibility Assessment," Geographies, MDPI, vol. 2(2), pages 1-13, June.

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