Details

Autor(en) / Beteiligte

• Ahmed, A. A. Masrur
• Deo, Ravinesh C.
• Feng, Qi
• Ghahramani, Afshin
• Raj, Nawin
• Yin, Zhenliang
• Yang, Linshan

Titel

Hybrid deep learning method for a week-ahead evapotranspiration forecasting

Ist Teil von

• Stochastic environmental research and risk assessment, 2022-03, Vol.36 (3), p.831-849

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2022

Quelle

Springer Journals

Beschreibungen/Notizen

• Reference crop evapotranspiration (ET o ) 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 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 o forecast. The method also assimilates a comprehensive dataset with 52 diverse predictors, i.e., satellite-derived moderate resolution imaging spectroradiometer, ground-based datasets from scientific information for landowners and synoptic-scale climate indices. To develop a vigorous CNN-GRU model, a feature selection stage entails the ant colony optimization method implemented to improve the ET o 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 o .