IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v108y2021i1d10.1007_s11069-021-04719-4.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-04719-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-021-04719-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. P. Santi & J. Manning & W. Zhou & P. Meza & P. Colque, 2021. "Geologic hazards of the Ocoña river valley, Peru and the influence of small-scale mining," 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(3), pages 2679-2700, September.
    10. 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.
    11. 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.
    12. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04719-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.