Early Estimation of Daily Reference Evapotranspiration Using Machine Learning Techniques for Efficient Management of Irrigation Water

Abstract

Assessing the right amount of water needs for a specific crop is a key task for farmers and agronomists to achieve efficient and optimal irrigation scheduling, and then an optimal crop yield. To this end, the reference evapotranspiration (ET0) was developed. It represents the atmospheric evaporation demand, and therefore an important variable for irrigation management. In this regard, several methods such as the FAO’s Penman-Monteith and Hargreaves have been used to model and estimate ET0. These methods use climatic parameters data for calculation procedures such as solar net radiation (Rn), saturation vapour pressure(es), and min-max air temperatures or a combination of them. In this paper, we investigated two proposed data-driven methods to predict ET0 values in a semi-arid region in Morocco. The first approach is based on forecasting techniques and the second one uses end-to-end modeling of ET0 based on meteorological data and machine learning models. The feature selection and engineering results show that solar global radiation (Rg) and mean air temperature (Ta) have a significance of more than 87% as relevant predictors features for the ET0. We then used them as input to machine learning regression models. Regression evaluation metrics showed that The XGboost regressor model performs well in both cross-validation with R2=0.93 in the first fold, and in hold-out validation with R2=0.92 and RMSE=0.55. As a final step, we compared the univariate time series forecasting of ET0 using the Facebook Prophet model versus the machine learning modeling method that we proposed. As goodness-of-fit measures, forecasting using machine learning modeling of ET0 showed better results in terms of both R2 and RMSE.