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

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

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.

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.
Data files:
  1. [1] Y. Mao, M. Zhang, P. Sun, and G. Wang, “Landslide susceptibility assessment using uncertain decision tree model in loess areas,” Environmental Earth Sciences, Vol.76, No.22, pp. 752-770, 2017.
  2. [2] K. Tokuda, “The application of robot technologies to disasters from torrential rains on Japan’s Kii Peninsula,” J. Robot. Mechatron., Vol.26, No.4, pp. 449-453, 2014.
  3. [3] Y. Mao, M. Zhang, G. Wang, and P. Sun, “Landslide hazards mapping using uncertain Naïve Bayesian classification method,” J. of Central South University, Vol.22, No.9, pp. 3512-3520, 2015.
  4. [4] G. Russ and R. Kruse, “Machine learning methods for spatial clustering on precision agriculture data,” Frontiers in Artificial Intelligence and Applications, Vol.227, pp. 40-49, 2011.
  5. [5] L. Gui, K. Yin, and J. Wang, “Landslide hazard zonation based on cluster analysis,” Hydrogeology and Engineering Geology, Vol.40, No.1, pp. 100-105, 2013.
  6. [6] K. Hu, P. Cui, Y. Han, and Y. You, “Susceptibility mapping of landslides and debris flows in 2008 Wenchuan earthquake by using cluster analysis and maximum likelihood classification methods,” Science of Soil and Water Conservation, Vol.10, No.1, pp. 12-18, 2012.
  7. [7] Y. Deng, Z. Zhang, N. Xie, and G. Lv, “Research on fuzzy clustering and its application,” Science of Surveying and Mapping, Vol.35, No.4, pp. 163-165, 2010.
  8. [8] J. Zhang, K. Yin, J. Wang, L. Liu, and F. Huang, “Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir,” Chinese J. of Rock Mechanics and Engineering, Vol.35, No.2, pp. 20-20, 2016.
  9. [9] R. Hayashi, “Desired robot technology in consideration of the records on natural disasters in Kyushu region,” J. Robot. Mechatron., Vol.26, No.4, pp. 442-448, 2014.
  10. [10] P. V. Gorsevski, P. E. Gessler, and P. Jankowski, “A fuzzy k-means classification and a bayesian approach for spatial prediction of landslide hazard,” M. M. Fischer and Arthur Getis (Eds.), “Handbook of Applied Spatial Analysis,” Springer, pp. 653-684, 2010.
  11. [11] Z. Long, C. Zhang, F. Liu, and Z. Zhang, “An improved chameleon algorithm,” Computer Engineering, Vol.35, No.20, pp. 189-191, 2009.
  12. [12] S. Jiang, G. Pang, and L. Zhang, “Enhanced chameleon clustering algorithm,” J. of Chinese Computer Systems, Vol.31, No.8, pp. 1644-1646, 2010.
  13. [13] M. S. Yang and Y. Nataliani, “Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters,” Pattern Recognition, Vol.71, No.6, pp. 45-59, 2017.
  14. [14] Y. Mao, Z. Peng, Z. Chen, and D. Liu, “Landslide hazard assessment based on uncertain decision tree classification method,” Application Research of Computers, Vol.31, No.12, pp. 3646-3650, 2014.
  15. [15] Y.-K. Yeon, J.-G. Han, and K. H. Ryu, “Landslide susceptibility mapping in Injae, Korea, using a decision tree,” Engineering Geology, Vol.116, Nos.3-4, pp. 274-283, 2010.
  16. [16] B. Pradhan and S. Lee, “Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model,” Earth Science Frontiers, Vol.14, No.6, pp. 143-152, 2007.
  17. [17] F. Guzzetti, A. Carrara, M. Cardinali, and P. Reichenbach, “Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy,” Geomorphology, Vol.31, Issues 1-4, pp. 181-216, 1999.
  18. [18] S. Kangping, T. Yuan, and W. Daming, “Comparison of three statistical methods on landslide susceptibility analysis: a case study of Shenzhen city,” J. of Peking University, Vol.45, No.4, pp. 640-646, 2009.
  19. [19] T. Henmi, “Control parameters tuning method of nonlinear model predictive controller based on quantitatively analyzing,” J. Robot. Mechatron., Vol.28, No.5, pp. 695-701, 2016.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on May. 22, 2019