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篇名
Uncertain GM-CFSFDP Clustering Algorithm for Landslide Hazard Prediction
並列篇名
Uncertain GM-CFSFDP Clustering Algorithm for Landslide Hazard Prediction
作者 Ruey-Shun Chen (Ruey-Shun Chen)Yeh-Cheng Chen (Yeh-Cheng Chen)
英文摘要
Due to difficulties in obtaining and effectively processing rainfall in landslide hazard prediction, as well as the existing limitation in dealing with large-scale data sets in clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, a novel CFSFDP algorithm based on grid and merging clusters (GM-CFSFDP) has been proposed to assess landslide susceptibility model. Firstly, this method adopted a new two-phase clustering algorithm, which is suitable for large-scale data sets. Secondly, the uncertain data model is presented to effectively quantify triggering factors (precipitation). At the same time, a novel Euclidean distance formula based on midpoint and length of uncertain data (E−ML distance formula) is designed, which makes the new method to manage the uncertain data. Finally, the prediction model of landslide hazards was constructed and verified in Baota district of Yan’an city. The experimental results show that the uncertain GM-CFSFDP clustering algorithm can effectively improve the accuracy of landslide hazard prediction.
起訖頁 067-079
關鍵詞 uncertain datalandslideGM-CFSFDP clustering algorithmhazard prediction
刊名 電腦學刊  
期數 202108 (32:4期)
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