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Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

Author

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  • Deborah Simon Mwakapesa

    (School of Civil, and Surveying, & Mapping, Jiangxi University of Science and Technology, Ganzhou 341000, China)

  • Yimin Mao

    (School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
    School of Information Engineering, Shaoguan University, Shaoguan 512005, China)

  • Xiaoji Lan

    (School of Civil, and Surveying, & Mapping, Jiangxi University of Science and Technology, Ganzhou 341000, China)

  • Yaser Ahangari Nanehkaran

    (School of Information Engineering, Yancheng Teachers University, Yancheng 224002, China)

Abstract

Landslide susceptibility mapping (LSM) studies provide essential information that helps various authorities in managing landslide-susceptible areas. This study aimed at applying and comparing the performance of DIvisive ANAlysis (DIANA) and RObust Clustering using linKs (ROCK) algorithms for LSM in the Baota District, China. These methods can be applied when the data has no labels and when there is insufficient inventory data. First, based on historical records, survey reports, and previous studies, 293 landslides were mapped in the study area and 7 landslide-influencing attributes were selected for modeling. Second, the methods were clustered in the study area mapping units into 469 and 476 subsets, respectively; for mapping landslide susceptibility, the subsets were classified into 5 susceptibility levels through the K-means method using landslide densities and attribute values. Then, their performances were assessed and compared using statistical metrics and the receiver operating curve (ROC). The outcomes indicated that similarity measures influenced the accuracy and the predictive power of these clustering models. In particular, when using a link-based similarity measure, the ROCK performed better with overall performance accuracy of 0.8933 and an area under the curve (AUC) of 0.875. The maps constructed from the models can be useful in landslide assessment, prevention, and mitigation strategies in the study area, especially for areas classified with higher susceptibility levels. Moreover, this comparison provides a new perspective in the selection of a considerable model for LSM in the Baota District.

Suggested Citation

  • Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4218-:d:1081158
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    1. Xuedong Zhang & Haoyun Xie & Zidong Xu & Zhaowen Li & Bo Chen, 2024. "Evaluating landslide susceptibility: an AHP method-based approach enhanced with optimized random forest modeling," 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. 120(9), pages 8153-8207, July.
    2. Li Zhuo & Yupu Huang & Jing Zheng & Jingjing Cao & Donghu Guo, 2023. "Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance," Sustainability, MDPI, vol. 15(11), pages 1-23, June.
    3. Sheng Ma & Jian Chen & Saier Wu & Yurou Li, 2023. "Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi," Sustainability, MDPI, vol. 15(22), pages 1-26, November.

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