JRM Vol.31 No.2 pp. 329-338
doi: 10.20965/jrm.2019.p0329


Using Uncertain DM-Chameleon Clustering Algorithm Based on Machine Learning to Predict Landslide Hazards

Jian Hu*, Haiwan Zhu**, Yimin Mao**, Canlong Zhang**, Tian Liang*, and Dinghui Mao***

*Applied Science Institute, Jiangxi University of Science and Technology
Hakka Avenue No.156, Zhanggong District, Ganzhou City, Jiangxi 341000, China

**Information Institute, Jiangxi University of Science and Technology
Hakka Avenue No.156, Zhanggong District, Ganzhou City, Jiangxi 341000, China

***211 Battalion, Co., Ltd., China Shanxi Nuclear Industry Group Company
Xi’an 710024, China

August 29, 2018
January 31, 2019
April 20, 2019
machine learning, uncertain data, landslides, chameleon algorithm, hazard prediction

Landslide hazard prediction is a difficult, time-consuming process when traditional methods are used. This paper presents a method that uses machine learning to predict landslide hazard levels automatically. Due to difficulties in obtaining and effectively processing rainfall in landslide hazard prediction, and to the existing limitation in dealing with large-scale data sets in the M-chameleon algorithm, a new method based on an uncertain DM-chameleon algorithm (developed M-chameleon) is proposed to assess the landslide susceptibility model. First, this method designs a new two-phase clustering algorithm based on M-chameleon, which effectively processes large-scale data sets. Second, the new E-H distance formula is designed by combining the Euclidean and Hausdorff distances, and this enables the new method to manage uncertain data effectively. The uncertain data model is presented at the same time to effectively quantify triggering factors. Finally, the model for predicting landslide hazards is constructed and verified using the data from the Baota district of the city of Yan’an, China. The experimental results show that the uncertain DM-chameleon algorithm of machine learning can effectively improve the accuracy of landslide prediction and has high feasibility. Furthermore, the relationships between hazard factors and landslide hazard levels can be extracted based on clustering results.

Location of the study area: Baota District, Yan

Location of the study area: Baota District, Yan"an City

Cite this article as:
J. Hu, H. Zhu, Y. Mao, C. Zhang, T. Liang, and D. Mao, “Using Uncertain DM-Chameleon Clustering Algorithm Based on Machine Learning to Predict Landslide Hazards,” J. Robot. Mechatron., Vol.31 No.2, pp. 329-338, 2019.
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Last updated on May. 19, 2024