Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 335-341, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-335-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 335-341, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-335-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

A HYBRID APPROACH FOR PREDICTION OF CHANGES IN LANDSLIDE RATES BASED ON CLUSTERING AND A DECISION TREE

J. Ma1, H. Tang2, T. Wen2, and X. Liu1 J. Ma et al.
  • 1Three Gorges Research Center for Geo-hazards of Ministry of Education, China University of Geosciences, Wuhan, Hubei 430074, PR China
  • 2Faculty of Engineering, China University of Geosciences, Wuhan, Hubei 430074, PR China

Keywords: Landslides, Prediction, Two-step cluster, Classification and regression tree (CART), White box model

Abstract. Forecasting the complex deformation patterns (e.g., displacement, velocity, etc.) of landslides is required to prevent property damage and loss of human lives caused by landslide deformation and failure. In this study, a hybrid approach with clustering and a decision tree is proposed to predict changes in landslide rates. The performance of the hybrid approach is evaluated using multi-parameter monitoring data from the Majiagou landslide, Three Gorges Reservoir, China. A forecasting model consisting of a set of clear, interpretable decision rules was created, and the model achieved a satisfactory accuracy. The results indicate that the hybrid data mining approach can be used to build an explicit representation of the cause-effect relationships hidden in large, complex data sets and generate novel predictions of changes in landslide rates. It is believed that the approach employed in this study could be easily utilized by several categories of users, from beginner to expert, and provide support to improve studies of landslide deformation forecasting. Additionally, the proposed approach could be implemented in other domains characterized by large, complex data sets and cause-effect relationships.