Simulation of Crop Evaportranspiration Based on BP Neural Network Model and Grey Relational Analysis

Full Text (PDF, 135KB), PP.15-21

Views: 0 Downloads: 0


Liang Ma 1,* Feng Liu 1 Liangliang Chen 1 Ming Hong 1

1. Xinjiang Agricultural University, Urumqi City, Xinjiang Province, China

* Corresponding author.


Received: 10 Nov. 2011 / Revised: 22 Dec. 2011 / Accepted: 25 Jan. 2012 / Published: 29 Feb. 2012

Index Terms

Arid zone, BP neural networks, crop evaportranspiration, sensitiveness factors, grey relational analysis


Crop evaportranspiration was studied with measured data of Kongque river irrigation district in Xinjiang Province based on application of BP neural networks, a sensitivity analysis about crop evaportranspiration was conducted according to each input factor by using default factor method, and the grey relational analysis method was applied to certify the results.The results showed that the artificial neural networks model could express quantitatively the response relationship between crop evaportranspiration and various factors with sufficient high accuracy. Soil moisture and solar radiation were the main sensitive factors for soil water-salt dynamic in this irrigation district, the interaction amongst various factors formed coupling relationship under the complicated condition. The grey relational analysis method could further verify the sensitivity degree amongst various factors. The combination of the above methods provides feasible method for analyzing the rules of crop water comsumption during crop growing season, and it is complement and perfection for the traditional research methods of crop evaportranspiration.

Cite This Paper

Liang Ma,Feng Liu,Liangliang Chen,Ming Hong,"Simulation of Crop Evaportranspiration Based on BP Neural Network Model and Grey Relational Analysis", IJEM, vol.2, no.1, pp.15-21, 2012. DOI: 10.5815/ijem.2012.01.03 


[1] Shang Songhao, Mao Xiaomin, Lei Zhidong.“Winter wheat field moisture forecast BP neural network model,”Journal of Hydraulic Engineering,vol. 33,no. 4,pp.60-63, April 2002.(in Chinese)

[2] Sollich P, Krogh A. Learning with Ensembles: How Over-fitting Can Be Useful. Advances in Neural Information Processing Systems, Denver, CO, Cambridge, MA: MIT Press, 1996.

[3] Pokrajac D, Obradovic Z.“Neural network-based software for fertilizer optimization in precision farming,” Neural Networks, vol. 40,no. 3,pp. 2110-2115, March 2001.(in Chinese)

[4] Joannis N Daliakopoulos, Pauline Coulibaly, Joannis K Tsanis. “Groundwater level forecasting using artificial neural networks,” Journal of Hydrology, vol. 309,no. 7,pp.229-240,July 2005.

[5] Emery Coppola Jr, Mary Poulton, Emmanuel Charles.“Application of artificial neural networks to complex groundwater management problems,”Natural Resources Research, vol. 12,no. 4,pp.303-320,April 2003.

[6] Beaudeau, P. Beaudeau, T. Leboulanger, et al.“Forecasting of turbid floods in a karstic drain using an artificial neural network,”Ground Water, vol. 39,no. 1,pp.109-119,January 2001.

[7] Liu Shuwen, Wang Qingwei, He Dongjian, et al. “Grape disease diagnosis system based on fuzzy neural network,”.Transactions of the CSAE, vol. 22,no. 4,pp. 66-69,April 2006.(in Chinese)

[8] LIU Sifeng, Xie Naiming.Grey system theory and its application. Beijing:Science Press, 2008.(in Chinese)