Selecting Essential Factors for Predicting Reference Crop Evapotranspiration Through Tree-based Machine Learning and Bayesian Optimisation

Reference crop evapotranspiration (ET O ) is a basic component of the hydrological cycle, and its estimation is critical for agricultural water resource management and scheduling. In this study, three tree-based machine learning algorithms (random forest [RF], gradient boosting decision tree [GBDT], and extreme gradient boosting [XGBoost]) were adopted to determine the essential factors for ET O prediction. The tree-based models were optimised using the Bayesian optimisation (BO) algorithm, and they were compared with three standalone models in terms of daily ET O and monthly mean ET O estimation in North China, with different input combinations of essential variables. The results indicated that solar radiation (R s ) and air temperature (T s ), including the maximum, minimum, and average temperature, in daily ET O were the key parameters affecting model prediction accuracy. R s was the most influential factor in the monthly average ET O model, followed by T s . Both relative humidity (RH) and wind speed at 2 m (U 2 ) had little impact on ET O prediction at different scales, although their importance differed. Compared with the GBDT and RF models, the XGBoost model exhibited the highest performance for daily ET O and monthly mean ET O estimation. The hybrid tree-based models with the BO algorithm outperformed the standalone tree-based models. Overall, compared with other inputs, the model with three inputs (R s , T s , and RH/U 2 ) had the highest accuracy. The BO-XGBoost model exhibited superior performance in terms of the global performance index (GPI) for daily ET O and monthly mean ET O prediction and it is recommended as a more accurate model predicting daily ET O and monthly mean ET O in North China or areas with a similar climate.