Time Sequence Forecast of Ground Settlement Based on WT-SVR-ARMA
Journal: International Journal of Science and Research (IJSR) (Vol.6, No. 4)Publication Date: 2017-04-05
Authors : Pengfei Wang;
Page : 2156-2160
Keywords : time sequence; wavelet transform; support vector regression; autoregressive moving average; ground settlement;
Abstract
Ground settlement caused by the subway underground excavation is a very critical issue. Considering the important significance of an accurate ground settlement prediction model to underground construction, a time series prediction method based on wavelet transform (WT) and support vector regression (SVR) and autoregressive moving average (ARMA) model to predict ground settlement is prosed. Wavelet transform is used to decompose and reconstruct ground settlement time sequence into trend sequence and random sequence. Trend sequence is forecasted by SVR model. Random sequence is forecasted by ARMA model. The final prediction values are the sum of trend sequence and random sequence prediction values. This method is used to the data sampled from sensors located at Ziyou Road station in Changchun and it shows that this method is valid and applicable.
Other Latest Articles
- Family Burden and Social Support of the Parents of Children with Hearing Impairment
- Exploring Combining Ability to Generate Peasant Farmers Preferred Hybrid Tomato Varieties and Hints on Seed Multiplication
- Seismic Analysis and Cost Estimation of GFRP and RCC Auditorium Building using ETABS
- Characteristic of Pore Pressure at the Sub Surface Sedimentary Rock in Deep Water Part of Kutai Basin, East Kalimantan, Indonesia
- Treatment of Dairy Wastewater by Physiochemical Method
Last modified: 2021-06-30 18:32:29