Sensitivity Analysis and Identification of Parameters to the Van Genuchten Equation

Van Genuchten equation is the soil water characteristic curve equation used commonly, and identifying (estimating) accurately its parameters plays an important role in the study on the movement of soil water. Selecting the desorption and absorption experimental data of silt loam from a northwest reg...

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Bibliographic Details
Main Authors: Guangzhou Chen, Lifeng Jiao, Xinhai Li
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Journal of Chemistry
Online Access:http://dx.doi.org/10.1155/2016/9879537
Description
Summary:Van Genuchten equation is the soil water characteristic curve equation used commonly, and identifying (estimating) accurately its parameters plays an important role in the study on the movement of soil water. Selecting the desorption and absorption experimental data of silt loam from a northwest region in China as an instance, Monte-Carlo method was firstly applied to analyze sensitivity of the parameters and uncertainty of model so as to get the key parameters and posteriori parameter distribution to guide subsequent parameter identification. Then, the optimization model of the parameters was set up, and a new type of intelligent algorithm-difference search algorithm was employed to identify them. In order to overcome the fault that the base difference search algorithm needed more iterations and to further enhance the optimization performance, a hybrid algorithm, which coupled the difference search algorithm with simplex method, was employed to identification of the parameters. By comparison with other optimization algorithms, the results show that the difference search algorithm has the following characteristics: good optimization performance, the simple principle, easy implement, short program code, and less control parameters required to run the algorithm. In addition, the proposed hybrid algorithm outperforms the basic difference search algorithm on the comprehensive performance of algorithm.
ISSN:2090-9063
2090-9071