Evapotranspiration estimation using deep learning models for robust sensor performance - Yassine Boukhali; Mohammed Nabil Kabbaj; Mohammed Benbrahim (Franklin Open)
Accurate daily estimation of reference evapotranspiration (ET0) is crucial for precision irrigation, particularly in semi-arid regions with variable climatic conditions such as Fez–Meknes. Traditional empirical methods often require complete meteorological inputs and operate over broad timescales, while recent machine-learning approaches rarely address real-world sensor failures or computational constraints. In this study, we simulate realistic IoT data loss in both weather feature-based missingness scenarios and time-based scenarios. We compare five models, including CatBoost, LightGBM, LSTM, GRU, and Bi-LSTM, on a Moringa oleifera irrigation dataset, evaluating estimation accuracy, computational cost, and resilience to missing data. Results show that Bi-LSTM achieves the highest test R2=0.9651 and lowest RMSE (0.0623 mm/day) under complete data, while CatBoost offers comparable accuracy (R2=0.9614, RMSE = 0.0655 mm/day) with sub-millisecond inference (0.0032 s per 200 samples). Importantly, CatBoost maintains test R2≥0.875 and RMSE ≤0.117 mm/day across all feature and time-based missingness scenarios. These findings demonstrate that CatBoost balances high accuracy with minimal computational resources and strong resilience to both sensor and time-interval data gaps, making it an ideal solution for fog-computing and rural irrigation management where data interruptions and hardware limitations are common.