Development of machine learning-based reference evapotranspiration model for the semi-arid region of Punjab, India

Abstract

Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET0) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET0) using limited meteorological inputs are critical in data-constrained circumstances, and the preferred FAO-56 Penman-Monteith (PM) equation cannot be used. To overcome the complexity of calculation, the present study is focused on developing a Random Forest-based ET0 model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the readily available data at the farm level. For comparative study Hargreaves–Samani model was also modified and used to estimate the ET0. Further, ET0 was also estimated using existing models like Hargreaves–Samani model and the Modified Panman model. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (2090 − 2010) and nine years (2011–2019), respectively, and the PM FAO-56 model was taken as a standard model. The developed RF-based model's mean absolute error and root-mean-square deviation were found to be better than the other models, and it was obtained as 0.95 mm/d and 1.32, respectively, with an r2 value of 0.92. The developed RF-based model was used to predict the ET0, and further, predicted ET0 values were used for irrigation scheduling of two growing seasons (2020–2021) of maize and wheat crops. The result of the field experiment also shows that there was no significant yield reduction in the crop. Hence, This developed study model can be used for the irrigation in the semiarid region of the Punjab India as well as other part of world. Also, it can be used as a replacement FAO-56 model.