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Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics

Author

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  • Qiang Liu

    (Harbin Institute of Technology)

  • Delong Huang

    (Harbin Institute of Technology)

  • Aiping Tang

    (Harbin Institute of Technology)

  • Xiaosheng Han

    (China University of Geosciences (Beijing))

Abstract

Considering the increasing number of landslides due to permafrost degradation, this paper reports a performance evaluation of three classical landslide susceptibility models applied to cold regions. A landslide inventory was first constructed through a historical survey and image interpretation. Ten causative factors of landslides were then chosen based on the available data and the local environment. Multicollinearity diagnosis and factor effectiveness test were employed to perform a factor analysis. Subsequently, three evaluation models based on the frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) were established. These models were applied to obtain landslide susceptibility maps, which were then analyzed and compared. The model performance was evaluated in terms of the accuracy rate and fluctuation characteristics. The results showed no multicollinearity issue between the factors employed. The annual temperature difference and frozen depth are two indispensable factors when assessing landslide susceptibility in cold regions. A comparison between the susceptibility maps generated using the three models showed that the FR model-generated map is most in line with the principle of disaster zoning and has the highest degree of conformity with actual landslide points, followed by the maps generated using the LR and ANN models. An accuracy analysis showed that the ANN model yields the highest AUC value in the training and test states, 0.957 and 0.863, respectively; however, these values were not optimal given the fluctuation. Moreover, the fluctuation in the non-landslide data was greater than that in the landslide data. The fluctuation results revealed the drawback of the AUC value in the analysis of the model performance. In other words, the non-landslide error often covers up the landslide error. This study provides a scientific guidance for evaluating the model performance and for assessing landslide disasters in cold regions.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04719-4
    DOI: 10.1007/s11069-021-04719-4
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Liu, Qiang & Tang, Aiping & Huang, Delong & Huang, Ziyuan & Zhang, Bin & Xu, Xiuchen, 2022. "Total probabilistic measure for the potential risk of regional roads exposed to landslides," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    2. Qiang Liu & Aiping Tang & Zhongyue Wang & Buyue Zhao, 2023. "Exploring the road icing risk: considering the dependence of icing-inducing factors," 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. 115(3), pages 2161-2178, February.

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