Hybrid Deep Learning for Week-Ahead Evapotranspiration Forecasting

Abstract

Abstract Reference evapotranspiration (ET) is an integral hydrological factor in soil-plant-atmospheric water balance studies and the management of drought events. This paper proposes a new hybrid-deep learning (DL) approach, combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) along with Ant Colony Optimization (ACO), for a multi-step (week 1 to 4) daily-ET forecast. The method also assimilates a comprehensive dataset with 52 diverse predictors, i.e., satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS), ground-based datasets from Scientific Information for Landowners (SILO) and synoptic-scale climate indices (CI). To develop a vigorous CNN-GRU model, a feature selection stage entails the Ant Colony Optimization (ACO) method implemented to improve the ET forecast model for the three selected sites in Australian Murray Darling Basin. The results demonstrate excellent forecasting capability of the hybrid CNN-GRU model against the counterpart benchmark models, evidenced by a relatively small mean absolute error and high efficiency. Overall, this study shows that the proposed hybrid CNN-GRU model successfully apprehends the complex and non-linear relationships between predictor variables and the daily ET.