A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms
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ORIGINAL PAPER
A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms Jian Hu 1,2 & Kaibin Xu 1 & Genglong Wang 3 & Youcun Liu 4 & Muhammad Asim Khan 1 & Yimin Mao 1,3 & Maosheng Zhang 3 Received: 16 December 2018 / Accepted: 26 May 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Because of the strong dependence on the values for the input parameters and the cluster shape, as well as the difficulties in quantifying the precipitation in constructing landslide susceptibility maps by employing existing clustering algorithms, we propose a novel method based on an Ordering Points to Identify the Clustering Structure (OPTICS) algorithm using the Hausdorff distance (OA-HD). The OA-HD algorithm distributes mapping units into many subclasses with similar characteristic values for topography and geology. To obtain more optimal subclasses, the HD was adopted to quantify precipitation. The Kmedoids algorithm grouped these subclasses into five susceptibility levels according to the values of landslide density in each subclass. Applying the innovative integrated algorithms to the study area significantly improves the landslide susceptibility assessment, especially in a large study area. The method suggests new insights for better assessing landslide susceptibility in a large study area. Keywords Landslide susceptibility mapping . Clustering algorithm . Geographic information system
Introduction Landslides are the most common natural hazards in Chinese loess areas. They can severely damage property and cause loss of lives (Torizin et al. 2018). Determining the probability of a landslide involves (1) intrinsic factors, such as geological and morphological features that make the slope susceptible to failure, and (2) extrinsic factors, such as rainfall, earthquakes, and human activities that may lead to instability (Luo and Liu 2018). In Chinese loess areas, almost * Yimin Mao [email protected] * Maosheng Zhang [email protected] 1
School of Information Engineering, Jiangxi University of Science and Technology, 86, Hongqi Ave, Ganzhou, Jiangxi, China
2
School of Applied Science, Jiangxi University of Science and Technology, Ganzhou, JiangXi, China
3
Key Laboratory for Geo-hazards in Loess Area, MLR, 438 E. Youyi Road, Xi’an 710054, Shaanxi, China
4
School of Resource and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou, JiangXi, China
all landslides are triggered by rainfall, with massive landslides usually occurring during the monsoon (Chen et al. 2016). Over the years, the government of China has developed various solutions to mitigate and prevent the losses due to landslides. One of the optimal measures is to develop a landslide susceptibility map, which is a spatial distribution of probabilities of landslide occurrences in a given area based on local geo-environmental factors (Chen et al. 2018; Wang et al. 2015). At present, a large number of techniques and methods have been proposed and developed for the assessment of suscept
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