Machine learning models for the estimation of monthly mean daily reference evapotranspiration based on cross-station and synthetic data

The estimation of reference evapotranspiration (ET0) is important in hydrology research, irrigation scheduling design and water resources management. This study explored the capability of eight machine learning models, i.e., Artificial Neuron Network (ANN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Spline (MARS), Support Vector Machine (SVM), Extreme Learning Machine and a novel Kernel-based Nonlinear Extension of Arps Decline (KNEA) Model, for modeling monthly mean daily ET0 using only temperature data from local or cross stations. These machine learning models were also compared with the temperature-based Hargreaves–Samani equation. The results indicated that the estimation accuracy of these machine learning models differed in various scenarios. The treebased models (RF, GBDT and XGBoost) exhibited higher estimation accuracy than the other models in the local application. When the station has only temperature data, the MARS and SVM models were slightly superior to the other models, while the ANN and HS models performed worse than the others. When there was no temperature data at the target station and the data from adjacent stations were used instead, MARS, SVM and KNEA were the suitable models. The results can provide a solution for ET0 estimation in the absence of complete meteorological data. doi: 10.2166/nh.2019.060 s://iwaponline.com/hr/article-pdf/doi/10.2166/nh.2019.060/610605/nh2019060.pdf Lifeng Wu Youwen Peng School of Hydraulic and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China Lifeng Wu Junliang Fan (corresponding author) Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Yangling 712100, China E-mail: nwwfjl@163.com Lifeng Wu Yicheng Wang State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China Junliang Fan College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China