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Effect of using various weighting methods in a process of landslide susceptibility assessment

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  • Kamila Hodasová

    (Comenius University in Bratislava)

  • Martin Bednarik

    (Comenius University in Bratislava)

Abstract

This study discusses the evaluation of the effect of using different weighting approaches in the process of landslide susceptibility assessment. Weighting process is needed, especially for landslide susceptibility assessment using bivariate statistical analysis, and can radically affect the resulting susceptibility map. The bivariate analysis belongs to a set of quantitative methods. The initial point for the bivariate analysis is selection and processing of input factors in the form of parametric maps, factors that play a dominant role in slope stability. The parametric maps were subsequently evaluated related to landslide inventory map. Another essential part of the bivariate analysis is the determination of the weight of the given input factors. Herein, four methods were applied to determine the weights of each class within reclassified input factors, as well as the total weight of the individual input factors. As a study area, the district of Kysucké Nové Mesto in Slovakia was chosen. Four prognostic maps were the result using entropy index, AHP method (AHP—analytic hierarchy process) (the input factors weighted as a whole), frequency ratio and landslide index (the weights were calculated for each class of input factor). Final landslide susceptibility maps were verified trough ROC curves (ROC—receiver operating characteristics). The accuracy of maps was ascertained by the size area (AUC—area under curve) under ROC curve. The highest accuracy was obtained for maps using weights calculated from the landslide index (88.5%) and the frequency ratio (88.4%).

Suggested Citation

  • Kamila Hodasová & Martin Bednarik, 2021. "Effect of using various weighting methods in a process of landslide susceptibility assessment," 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. 105(1), pages 481-499, January.
  • Handle: RePEc:spr:nathaz:v:105:y:2021:i:1:d:10.1007_s11069-020-04320-1
    DOI: 10.1007/s11069-020-04320-1
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    1. Mustafa Kamal & Baolei Zhang & Jianfei Cao & Xin Zhang & Jun Chang, 2022. "Comparative Study of Artificial Neural Network and Random Forest Model for Susceptibility Assessment of Landslides Induced by Earthquake in the Western Sichuan Plateau, China," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    2. Helen Cristina Dias & Marcelo Fischer Gramani & Carlos Henrique Grohmann & Carlos Bateira & Bianca Carvalho Vieira, 2021. "Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast," 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 205-223, August.
    3. Xueling Wu & Junyang Wang, 2023. "Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China," IJERPH, MDPI, vol. 20(6), pages 1-18, March.

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