A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

作者：Hanmi Zhou, Linshuang Ma, Xiaoli Niu, Youzhen Xiang, Jiageng Chen, Yumin Su, Jichen Li, Sibo Lu, Cheng Chen, Qi Wu · 发表于：Agricultural Water Management · 年份：2024 · DOI：10.1016/j.agwat.2024.108807 · 被引用次数：29 · 研究领域：Plant Water Relations and Carbon Dynamics、Hydrology and Watershed Management Studies、Hydrological Forecasting Using AI

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 ...