Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges Reservoir area, China - Ma, Junwei; Tang, Huiming; Liu, Xiao; Hu, Xinli; Sun, Miaojun; Song, Youjian (Landslides, 2017)
This study presents a hybrid approach based on two-step cluster and decision tree C5.0 algorithms to establish a deformation forecasting model for a step-like landslide. The Zhujiadian landslide, a typical step-like landslide in the Three Gorges Reservoir area, was selected as a case study. Approximately , 6 years of historical records of landslide displacement, precipitation , and reservoir level were used to build the forecasting model. The forecasting model consisted of seven comprehensive rules governing hydrologic parameters and their magnitudes and was developed to predict landslide deformation. This model was applied to rapidly forecast the likelihood of step-like landslide deformation resulting from rainfall and water level fluctuations in the Three Gorges Reservoir area. Given the satisfactory accuracy of the trained model, the presented approach can be used to establish forecasting models for step-like landslides and to facilitate rapid decision making.