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Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping

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Abstract

Generation of landslide susceptibility maps is important for engineering geologists and geomorphologists. The goal of this study is to generate a reliable susceptibility map based on digital elevation modeling and remote sensing data through clustering technique. This study focused on the landslide problems on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Two stages of analysis were used to extract the dominant attributes and thresholds: (1) calculate the entropy with regard to the measure of influenced variables to the occurrence of landslide and (2) use the clustering analysis K-means with particle swarm optimization (KPSO) to resolve the difficulties in creating landslide susceptibility maps. The knowledge scope with regard to core factors and thresholds are solved. The self-organization map (SOM) is used as a parallel study for comparison. The overall accuracy of the susceptibility map is 86 and 77 % for KPSO and SOM, respectively. Then, the susceptibility maps are drawn and verifications made. The generation of a susceptibility map is useful for decision makers and managers to handle the landslide risk area.

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Acknowledgments

The author would like to express gratitude to the Director Dr. Chou of the GIS Research Center, Fang Chia University, for providing all the relevant data on the Shei-Pa National Park in Taiwan. National Science Council (98-2625-M-275-001 and 100-2410-H-275-009) also sponsored this work.

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  1. Department of Information Management, Ling Tung University, Taichung, Taiwan

    Shiuan Wan

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  1. Shiuan Wan
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Correspondence to Shiuan Wan.

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This work was submitted to Environmental Geology. A study performed by National Science Council Research Project: 98-2625-M-275-001 and 100-2410-H-275-009.

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Wan, S. Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping. Environ Earth Sci 68, 1349–1366 (2013). https://doi.org/10.1007/s12665-012-1832-7

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