Performance evaluation of soft computing techniques for forecasting daily reference evapotranspiration


 Precise information on evapotranspiration aids in efficient irrigation scheduling and optimal crop production. In this study, soft computing tools, namely, artificial neural network (ANN) and k-nearest-neighbor (KNN) models, were evaluated by comparing with the Penman–Monteith (PM) model using climatic data from 1990 to 2020 of the Indian Agricultural Research Institute (IARI) farm, New Delhi, India. Results revealed that the ANN model with sigmoid activation function and the L-BFGS learning algorithm was selected as the best-performing model among 36 ANN models tested in this study. Among all four KNN models tested, the K4 KNN model was observed to be the best in forecasting daily ET0. Overall, the best ANN model (M11) outperformed the K4 KNN model with mean absolute error, mean squared error, r, mean absolute percentage error, and d values of 0.075, 0.018, 0.997, 2.76%, and 0.974, respectively, during the training period and 0.091, 0.053, 0.984, 3.16%, and 0.969, respectively, during the testing period. Sensitivity analysis of the best ANN model revealed that wind speed was the most influencing input variable compared to other weather parameters. Thus, the ANN model to forecast daily ET0 accurately for efficient irrigation scheduling of different crops grown in the study region can be recommended.