DocumentCode
2646205
Title
Notice of Retraction
Analysis on the percolation from root zone of winter wheat: Combination of a numerical model and BP Artificial Neural Network
Author
Chen Haorui ; Huang Jiesheng ; Ding Xingyan ; Wu Jingwei
Author_Institution
State Key Lab. of Water Resources & Hydropower Eng. Sci., Wuhan Univ., Wuhan, China
Volume
7
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This study attempts to analyze the percolation from root zone of winter wheat combining a numerical model and BP Artificial Neural Network(BP-ANN).Based on field experimental data during winter wheat seasons from 2007 to 2009 in Shijing Irrigation Scheme in Hebei province in China, a numerical model was developed to analyze the percolation, employing Hydrus-1d soft package. A BP Artificial Neural Network (BP-ANN) regression model for percolation was then designed on the basis of comparisons of different algorithms and a variety of hidden unit numbers. Sample data for BP-ANN establishment were obtained by simulations in the numerical model of eight hundred and sixty-four input scenarios which were formed by combining five percolation-influenced factors (initial 2m-soil storage saturation, the date and amount of 1st Spring-Irrigation, 2nd Spring-Irrigation volume and time intervals between two Spring-Irrigation activities). Still, the specified BP-ANN was also compared to a multiple liner regression model (MLRM). The results showed :( 1) induced by two Spring-Irrigation activities, a double humps-shape in percolation process was formed, which overlapped with each other. Cumulative percolation in the whole growing season of winter wheat was up to almost 30% of the sum of irrigation and precipitation due to flood irrigation, (2) Levenberg-Marquardt(LM) algorithm was better than both the traditional and improved BP ones in this study. The most appropriate hidden unit number is 10 and the best structure for BP-ANN was - -10-1, which showed a higher computational accuracy than multiple liner regress model (MLRM).
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This study attempts to analyze the percolation from root zone of winter wheat combining a numerical model and BP Artificial Neural Network(BP-ANN).Based on field experimental data during winter wheat seasons from 2007 to 2009 in Shijing Irrigation Scheme in Hebei province in China, a numerical model was developed to analyze the percolation, employing Hydrus-1d soft package. A BP Artificial Neural Network (BP-ANN) regression model for percolation was then designed on the basis of comparisons of different algorithms and a variety of hidden unit numbers. Sample data for BP-ANN establishment were obtained by simulations in the numerical model of eight hundred and sixty-four input scenarios which were formed by combining five percolation-influenced factors (initial 2m-soil storage saturation, the date and amount of 1st Spring-Irrigation, 2nd Spring-Irrigation volume and time intervals between two Spring-Irrigation activities). Still, the specified BP-ANN was also compared to a multiple liner regression model (MLRM). The results showed :( 1) induced by two Spring-Irrigation activities, a double humps-shape in percolation process was formed, which overlapped with each other. Cumulative percolation in the whole growing season of winter wheat was up to almost 30% of the sum of irrigation and precipitation due to flood irrigation, (2) Levenberg-Marquardt(LM) algorithm was better than both the traditional and improved BP ones in this study. The most appropriate hidden unit number is 10 and the best structure for BP-ANN was - -10-1, which showed a higher computational accuracy than multiple liner regress model (MLRM).
Keywords
backpropagation; crops; geophysics computing; irrigation; neural nets; percolation; regression analysis; soil; BP artificial neural network; China; Hebei province; Hydrus-1d soft package; Levenberg-Marquardt algorithm; Shijing Irrigation Scheme; multiple liner regression model; numerical model; percolation analysis; soil storage saturation; spring irrigation volume; winter wheat root zone; Agricultural engineering; Artificial neural networks; Equations; Hydroelectric power generation; Irrigation; Laboratories; Numerical models; Soil; Water pollution; Water resources; BP Artificial Neural Network; numerical model; soil water percolation; winter wheat;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5485259
Filename
5485259
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