Innovative multi-temporal evapotranspiration forecasting using empirical fourier decomposition and bidirectional long short-term memory

Abstract

Reference evapotranspiration (ETo) is an essential environmental variable that is intimately significant to agriculture. Managing water and crop planning relies heavily on precise forecasting of ETo. This research used a novel time series decomposition technique, Empirical Fourier Decomposition (EFD), to forecast ETo accurately. Four machine learning techniques were used to forecast ETo using decomposed lagged ETo values. The input data source was from Prince Edward Island (PEI) weather stations (Harrington and St Peters Stations). First, autocorrelation analysis was performed to determine effective lags. Then, ETo data were decomposed using EFD, and lagged data was created based on EFD results. The Kbest feature selection algorithm was used to choose effective inputs, reducing the training time. The accuracy of models was evaluated using different statistical metrics such as correlation coefficient (R) and root mean square error (RMSE). The results showed that using EFD decomposition can significantly improve forecast accuracy. The comparison between different machine learning models showed that the deep learning-based model (Bidirectional LSTM (Long Short Term Memory)) (R = 0.956, RMSE= 0.451 mm/day for Harrington station and R = 0.956, RMSE= 0.451 mm/day for St Peters station) performed better than the Generalized Regression Neural Network (GRNN), K-nearest neighbor (KNN), and Random Forest (RF) models. Finally, the best model (EFD-Bidirectional LSTM) was used to forecast multitemporal ETo at both stations. Results showed that the developed model can forecast ETo for up to 28 days with reasonable accuracy. However, the accuracy of multi-step ahead forecasting decreases when evapotranspiration values are high, as the models tend to underestimate these values. The findings of this study can assist in accurately calculating crop water requirements and help farmers optimize their irrigation schedules.

Keywords

• Evapotranspiration
• Empirical fourier decomposition
• Machine learning
• Deep learning
• Climate adaptation
• Feature selection