Application and evaluation of a DENCLUE clustering algorithm for landslide susceptibility mapping

Jingguo Dai, Deborah Simon Mwakapesa

Abstract


Abstract: In this research, a clustering algorithm named the DENsity-based CLUstEring (DENCLUE) algorithm is applied and evaluated for landslide susceptibility mapping in Baota District, China.  The proposed methodology works well with large datasets, can handle noise effectively, and can obtain clusters of different types.  A dataset containing landslide records and 7 landslide influencing factors was prepared for modeling.  453 well-scattered clusters of various shapes and sizes were obtained from clustering the study area mapping units using the DENCLUE algorithm.  The natural breaks method was adopted to classify the clusters into 5 susceptibility classes using landslide density, eigenvalues, and geology expertise.  A map was then constructed showing landslide susceptibility in the area, which presented a significant assessment of landslide susceptibility in the Baota District.  Moreover, the model was evaluated and compared with DBSCAN, K-means, and KPSO clustering algorithms based on statistic metrics.  The results indicated that DENCLUE obtained the highest performance, and was thus, the best among others.  The constructed map can serve as a tool to identify safety areas within the Baota district, which are suitable for habitation and economic activities.

Keywords: Landslide, Landslide susceptibility mapping, Clustering, DENCLUE algorithm, Baota

DOI: 10.33440/j.ijpaa.20220501.191

 

Citation: Dai J G, Mwakapesa D S.  Application and evaluation of a DENCLUE clustering algorithm for landslide susceptibility mapping.  Int J Precis Agric Aviat, 2022; 5(1): 35–40.

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