Performance evaluation of soft computing techniques for forecasting daily reference evapotranspiration

Abstract

Reference evapotranspiration (ET0) is used to determine crop water requirements under different climatic conditions. In this study, soft computing tools viz. artificial neural network (ANN) and k-nearest neighbors (KNN) models were evaluated for forecasting daily ET0 by comparing their performance with the Penman-Monteith model (PM) using climatic data from 1990 to 2020 of the Indian Agricultural Research Institute (IARI) farm observatory, New Delhi, India. The performance of these models was assessed using statistical performance indices viz., mean absolute error (MAE), mean squared error (MSE), correlation coefficient (r), mean absolute percentage error (MAPE), and index of agreement (d). Results revealed that the ANN model with sigmoid activation function and L-BFGS (Limited memory-Broyden-Fletcher-Goldfarb-Shanno) learning algorithm was selected as the best performing model amongst 36 ANN models. Amongst 4 KNN models developed and 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 MAE, MSE, r, MAPE, and d values of 0.075, 0.018, 0.997, 2.76 %, and 0.974, respectively and 0.091, 0.053, 0.984, 3.16 %, and 0.969, respectively during training and testing periods. Thus, we conclude that the ANN technique performed better than the KNN technique in forecasting daily ET0. Sensitivity analysis of the best ANN model revealed that wind speed was the most influential input variable compared to other weather parameters. Thus, the ANN model to forecast daily ET0 accurately for efficient irrigation scheduling of different crops in the study region may be recommended. HIGHLIGHTS Regression analysis was performed for input variables selection.; In this study, the performance of 36 ANN models and 4 KNN models was evaluated.; The ANN model with sigmoid activation function and L-BFGS algorithm was selected as the best-performing model.; The best ANN model (M11) outperformed the K4 KNN model to forecast daily ET0.; Sensitivity analysis revealed that wind speed was the most influencing input variable.;

Keywords

• artificial neural network (ann)
• iari new delhi
• k-nearest neighbors (knn)
• mean absolute error (mae)
• sensitivity analysis