Application of Artificial Neural Network in Predicting Farmers’ Response to Water Management Decisions on Wheat Yield - Hardaha, M K; Chouhan, S S; Ambast, S K (Journal of Agricultural Engineering, 2013)
Water management usually involves decision-making with respect to allocation, scheduling and application ofavailable water to different crops over an irrigation season so as to get maximum economic returns. A study wascarried out in the Kaithal irrigation circle for prediction of farmers’ decisions regarding total depth of irrigationwater, fraction of groundwater and delay in sowing on yield of wheat crop under varying conditions ofgroundwater and soil salinity using Artificial Neural Networks (ANN). Three ANN algorithms i.e. gradientdescentback propagation (BP), Levenberg-Marquardt (LM) and radial basis functions (RBF) with variousarchitectures were used. It was found that radial basis function with a spread constant of 0.1 performed betterin predicting wheat yield. Also, it was observed that ANN algorithm predicted wheat and rice yields bettercorrelated to observed yields (r2=0.63 and 0.74) in comparison to regression model (r2=0.37 and 0.52)