Application of Artificial Neural Network in Predicting Farmers’ Response to Water Management Decisions on Wheat Yield
DOI:
https://doi.org/10.52151/jae2012493.1484Keywords:
Artificial neural network, conjunctive use, radial basis function, rice-wheat, saline waterAbstract
Water management usually involves decision-making with respect to allocation, scheduling and application of available water to different crops over an irrigation season so as to get maximum economic returns. A study was carried out in the Kaithal irrigation circle for prediction of farmers’ decisions regarding total depth of irrigation water, fraction of groundwater and delay in sowing on yield of wheat crop under varying conditions of groundwater and soil salinity using Artificial Neural Networks (ANN). Three ANN algorithms i.e. gradient-descent back propagation (BP), Levenberg-Marquardt (LM) and radial basis functions (RBF) with various architectures were used. It was found that radial basis function with a spread constant of 0.1 performed better in predicting wheat yield. Also, it was observed that ANN algorithm predicted wheat and rice yields better correlated to observed yields (r2=0.63 and 0.74) in comparison to regression model (r2=0.37 and 0.52)References
Ambast S K; Tyagi N K; Agrawal A; Sharma D K; Sakthivadivel R. 2004. Analysis of water allocation and distribution on farm level productivity in marginal quality ground water area under scares canal water supply conditions. Proceedings International Conference on Sustainable Management of Sodic Lands. UPCAR, Lukhnow, India, pp: 514-518.
Asadollahfardi G; Taklify A; Ghanbari A. 2012. Application of artificial neural network to predict TDS in Talkhed Rud River. J. Irrig. Drain. Eng., 138(4), 363-370.
Biswas A K. 1994. Consideration for sustainable irrigation development in Asia. Water Resou. Development, 10(4), 457-474.
Chakroborty K; Malhotra K; Mohan C K; Rank S. 1992. Forecasting the behaviour of multivariate time series using neural networks. Neural Networks, 5, 961-970.
Deck S H; Morrow C T; Heinemann D H; Sommer, H J. 1995. Comparison of a neural network and a traditional classifier for machine vision inspection of potatoes Appl. Eng. Agric., 11(2), 319-326.
Edossa D C; Baghel M S. 2011. Application of ANN based stream flow forecasting model for agricultural water management in the Awash River Basin, Ethiopia. Water Resou. Manage., 25(6), 1759-1773.
Gallant S I. 1986. Three constructive algorithms for network learning. Proceedings of 8th Annual Conference of Cognition Science Society, Michigan, 652-660.
Kwok T; Yeung D. 1997. Constructive algorithms for structure learning in feed-forward neural networks for regression problems. IEEE Trans. Neural Networks, 8(3), 630-645.
Lenton R. 1994. Consideration for sustainable irrigation development in Asia. Water Resou. Development, 10(4), 417-424.
Molden D; Bin D; Loeve R; Barker R; Tuong T P. 2007. Agricultural water productivity and savings: policy lessons from two diverse sites in China. Water Policy, 9(1), 29-44.
Tyagi N K; Agrawal A; Sakthivadivel R; Ambast S K; Sharma D K. 2004. Productivity of rice-wheat cropping system in a part of Indo-Gengetic plain: a spatial analysis. Irrig. Drain. Syst., 18(1), 73-88.
Wen C G; Lee C S. 1998. A neural network approach to multi-objective optimization for water quality management in river basin. Water Resour. Res., 34(3), 427-436.
Wang D. 2008. Fast constructive covering algorithm for neural networks and its implement in classification. Appl. Soft Comput., 8, 166-173.
Downloads
Published
Issue
Section
How to Cite
