期刊
NEURAL COMPUTING & APPLICATIONS
卷 30, 期 12, 页码 3825-3835出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-017-2968-x
关键词
Landslide displacement computing; Least squares support vector machine; Double exponential smoothing; Three Gorges Reservoir
资金
- National Basic Research Program (973 Program) [2013CB733200, 2014CB744703]
- Funds for Creative Research Groups of China [41521002]
- National Natural Science Foundation of China [41502293]
A novel hybrid model composed of least squares support vector machines (LSSVM) and double exponential smoothing (DES) was proposed and applied to calculate one-step ahead displacement of multifactor-induced landslides. The wavelet de-noising and Hodrick-Prescott filter methods were used to decompose the original displacement time series into three components: periodic term, trend term and random noise, which respectively represent periodic dynamic behaviour of landslides controlled by the seasonal triggers, the geological conditions and the random measuring noise. LSSVM and DES models were constructed and trained to forecast the periodic component and the trend component, respectively. Models' inputs include the seasonal triggers (e.g. reservoir level and rainfall data) and displacement values which are measurable variables in a specific prior time. The performance of the hybrid model was evaluated quantitatively. Calculated displacement from the hybrid model is excellently consistent with actual monitored value. Results of this work indicate that the hybrid model is a powerful tool for predicting one-step ahead displacement of landslide triggered by multiple factors.
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