Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

Evapotranspiration (ET) is widely employed to measure amounts of total water loss between land and atmosphere due to its major contribution to water balance on both regional and global scales. Considering challenges to quantifying nonlinear ET processes, machine learning (ML) techniques have been increasingly utilized to estimate ET due to their powerful advantage of capturing complex nonlinear structures and characteristics. However, limited studies have been conducted in subhumid climates to simulate local and spatial ETo using common ML methods. The current study aims to present a methodology that exempts local data in ETo simulation. The present study, therefore, seeks to estimate and compare reference ET (ETo) using four common ML methods with local and spatial approaches based on continuous 17-year daily climate data from six weather stations across the Red River Valley with subhumid climate. The four ML models have included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF) with three input combinations of maximum and minimum air temperature-based (Tmax, Tmin), mass transfer-based (Tmax, Tmin, U: wind speed), and radiation-based (Rs: solar radiation, Tmax, Tmin) measurements. The estimates yielded by the four ML models were compared against each other by considering spatial and local approaches and four statistical indicators; namely, the root means square error (RMSE), the mean absolute error (MAE), correlation coefficient (r(2)), and scatter index (SI), which were used to assess the ML model's performance. The comparison between combinations showed the lowest SI and RMSE values for the RF model with the radiation-based combination. Furthermore, the RF model showed the best performance for all combinations among the four defined models either spatially or locally. In general, the LR, GEP, and SVM models were improved when a local approach was used. The results showed the best performance for the radiation-based combination and the RF model with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among the models and approaches.