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

Deborah Simon Mwakapesa, Yimin Mao, Xiaoji Lan () and Yaser Ahangari Nanehkaran
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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
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

Sustainability, 2023, vol. 15, issue 5, 1-20

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.

Keywords: landslide; landslide susceptibility mapping; disasters; machine learning; clustering; ROCK algorithm; DIANA algorithm; Baota District (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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