Soil Water Simulation and Predication Using Stochastic Models Based on LS-SVM for Red Soil Region of China
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- 作者：Jianqiang Deng (12) djq1djq2@sina.com.cn
Xiaomin Chen (1) xmchen@njau.edu.cn
Zhenjie Du (1)
Yong Zhang (1) - 关键词：Soil water models – ; Chaotic analysis – ; Artificial intelligence – ; De ; noising methods – ; Wavelet
- 刊名：Water Resources Management
- 出版年：2011
- 出版时间：September 2011
- 年：2011
- 卷：25
- 期：11
- 页码：2823-2836
- 全文大小：405.2 KB
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- 刊物类别：Earth and Environmental Science
- 刊物主题：Earth sciences
Hydrogeology
Geotechnical Engineering
Meteorology and Climatology
Civil Engineering
Environment - 出版者：Springer Netherlands
- ISSN：1573-1650
文摘
The seasonal drought and the low available soil moisture affect the agricultural production in red soil region, China. Therefore, it is necessary to simulate and predict the dynamic changes of soil water in the field. Presently, dynamic model has been applied to obtain the soil water information. While the simulation accuracy of dynamic model depends on many complicated parameters, which are difficult to obtain. In this study, the various nonlinear Stochastic Model of soil water simulation systems and chaotic time series analysis methods of prediction systems had been set up. In the nonlinear Stochastic Model of soil water simulation systems, the daily soil water content simulated by Least squares support vector machine (LS-SVM) with the meteorological factors had more stabilities and advantages in soil water simulation performance over the Back Propagation Artificial Neural Network (BP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). In chaotic time series analysis method of prediction systems, the various signal preprocessing methods including the appropriate de-noising methods and wavelet decomposition methods were applied to preprocess the original chaotic soil water signal. The results of the prediction systems showed that the appropriate de-noising methods and the tendency of wavelet transformation had less effect on the delay time (τ) and embedding dimension (m). The de-noising methods may ignore the detail information of the soil water signal, while the appropriate wavelet transformation to get smaller Maximum Lyapunov Exponent (λ1) of the chaotic soil water signal detail and tendency information can improve the predicting capacity.