Artificial Neural Network Models to Predict Wheat Crop Evapotranspiration

Abstract

The development of Artificial Neural Network (ANN) models for prediction of wheat crop evapotranspiration using measured weather data and lysimeter measured crop evapotranspiration (ETc) for Delhi is described. Eleven meteorological variables were taken into consideration for this study. ANN models were developed in MATLAB© with different network architectures using Feed Forward Back Propagation (FFBP) and Elman Back Propagation (EBP) algorithms. The total length of data record used was 744, out of that 60% was taken for model training, 20% for model testing and remaining 20% for model validation. Training and testing data sets were used for model development purpose, while validation data set was used for model evaluation. The ANN modelling strategy having back propagation learning algorithm, log-sigmoid transfer function and model input strategy-1 exhibited better results with Nash-Sutcliffe Coefficient (E) and Root Mean Square Error (RMSE) of 0.972 and 0.498 mm for development data set and 0.776 and 1.334 mm for evaluation data set, respectively. FAO Penman-Monteith method was also used to estimate evapotranspiration. Comparison of the ANN predicted ETc and FAO Penman-Monteith estimated ETc with lysimeter values showed that the ANN predicted ETc was more close to the lysimeter measured values.