Abstract
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)
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