DocumentCode
2070792
Title
The prediction of soil moisture based on rough set-neural network model
Author
Yan Wen ; Liu Wending ; Cheng Zhen ; Kan Jiangming
Author_Institution
Acad. of Eng., Beijing Forestry Univ., Beijing, China
fYear
2010
fDate
29-31 July 2010
Firstpage
2413
Lastpage
2415
Abstract
According to the analysis of meteorological parameters affecting the soil moisture, this paper puts forward a new prediction model of soil moisture based on rough set and neural network. Reduce the attribute of decision table and pick up key factors as input of artificial neural network. neural network is used as a function approximation to train the data set and build estimation model. It is shown that this hybrid method can reduce the training time, improve the learning efficiency, enhance the predication accuracy, and be feasible and effective.
Keywords
decision tables; geophysics computing; geotechnical engineering; neural nets; rough set theory; soil; decision table attribute reduction; learning efficiency improvement; meteorological parameters; rough set neural network model; soil moisture prediction; training time reduction; Algorithm design and analysis; Artificial neural networks; Decision making; Irrigation; Predictive models; Soil moisture; Training; Neural Network; Rough Set; Soil Moisture;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2010 29th Chinese
Conference_Location
Beijing
Print_ISBN
978-1-4244-6263-6
Type
conf
Filename
5572030
Link To Document