River Water Turbidity Forecasting Based on Phase Space Reconstruction and Support Vector Regression

摘要：

Due to the nonlinear and nonstattonary of river water turbidity, a novel hybrid forecasting model based on phase space reconstruction and support vector regression (PSR-SVR) is proposed. Firstly, the embedding dimension is chosen by using the false nearest neighbor method, and the time delay is obtained by the average mutual information. The phase space is reconstructed from the time series with the embedding dimension and the time delay got. The reconstructed time array is used as the input signal of support vector regression network. Then the forecasting model is established. Utilizing the model to forecast the river water turbidity, and it shows the accuracy of this new forecasting model is superior to RBF and BP forecasting methods.

关键词： Water quality River water turbidity Forecasting Support vector regression Phase space reconstruction

作者: WANG Jun-dong LI Pei-yan ZHANG Yong-ming QI Wei-gui

作者单位: Harbin Institute of Technology, Harbin,150001, China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 2579-2582

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）