A generalization of the M5 Tree model and artificial neural network model for estimating daily reference evapotranspiration under minimal climatic data conditions

ABSTRACT To enhance water use efficiency in agriculture, accurately estimating plant water consumption is essential. This study examines the ability of artificial neural networks (ANN) and decision tree models to calculate reference evapotranspiration (ET0). Data from Shahrekord, Farokhshahr, and Saman stations (2004–2013), including temperature, humidity, wind speed at two meters, and sunshine hours, were used. The FAO Penman-Monteith model evaluated the models. In the first phase, eight scenarios were designed to estimate evapotranspiration under different climatic data. Evaluation metrics such as RMSE, MAE, and correlation coefficient (R) were calculated. Results showed that the ANN model, with a feedforward network and Levenberg-Marquardt algorithm, outperformed the M5 decision tree model at all stations: (RMSE = 0.3, MAE = 0.23, R = 0.99) at Shahrekord airport, (RMSE = 0.34, MAE = 0.26, R = 0.99) at Farokhshahr, and (RMSE = 0.31, MAE = 0.24, R = 0.99) at Saman. In the second phase, both models were trained with Farokhshahr data and tested with data from the other stations. Results again showed the superior performance of the ANN model. Overall, the study concluded that the ANN model outperforms the M5 model in data-scarce conditions, making it a better tool for water resource management.