A Landslide Susceptibility Mapping Using CIBD-CURE Algorithm and LEPAM Methods for Baota District, China

Abstract

Landslide susceptibility mapping (LSM) is one of the crucial steps in managing and mitigating landslides. This study targets at developing a new LSM model for Baota District in China, using a new clustering method namely CIBD-CURE, which combines the city block distance (CIBD) and traditional clustering using representatives (CURE) algorithm. It aims at addressing limitations of inability to identify clusters (subclasses) with arbitrary shapes and varying sizes, sensitivity to noise, inability to perform well in large study areas with large dataset and inability to process rainfall (uncertain) data, which affect the results of traditional clustering algorithms in LSM. The CIBD was introduced into the CURE algorithm for processing uncertain data, then CIBD-CURE partitioned the mapping units into arbitrary shaped and sized subclasses with respect to their underlying geology and topography characteristics, and handled noise successfully. Furthermore, LEPAM method was proposed to sort the subclasses into five landslide susceptibility levels. Finally, standard statistical measures were applied to evaluate the model’s performance and compared it against CURE, AHC-OLID, HC and KPSO clustering models, along with DTU and NBU classification models. The result analysis showed data the proposed model attained higher performance. This LSM study will be useful for landslides management strategies not only for this study area but also other affected areas around the world.