Hybrid Deep Learning for Climate-Driven Atmospheric Irrigation Potential Forecasting: A Case Study for Ankara

Year 2026, Volume: 13 Issue: 1 , 200 - 214 , 31.03.2026

Murat Akın

https://doi.org/10.54287/gujsa.1831069

https://izlik.org/JA52ZX44BT

Abstract

Accurate forecasting of atmospheric irrigation potential under variable climate conditions is essential for sustainable water management in semi-arid regions. In this study, monthly atmospheric irrigation potential for Ankara, Türkiye was analyzed using a hybrid residual model that combines a Long Short-Term Memory (LSTM) network with an Extreme Gradient Boosting (XGBoost) algorithm. Daily climate data from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) archive for 2001–2024 and reference evapotranspiration (ET₀) values calculated using the Food and Agriculture Organization (FAO) 56 Penman–Monteith equation formed the basis of the predictors. The LSTM model learned the temporal structure of the climatic inputs, and XGBoost was then applied to represent the remaining residual errors by incorporating extended climatic and seasonal features. Testing on the 2022–2024 period, the hybrid model produced a Root Mean Square Error (RMSE) of 24.4 mm, a Mean Absolute Error (MAE) of 20.3 mm and a Coefficient of Determination (R²) of 0.87. Feature-importance results showed that Month Sine and last-step ET₀ had the strongest influence, followed by relative humidity, wind speed and shortwave radiation. Although the accuracy gains over the standalone models were moderate, the hybrid design delivered more stable forecasts and a clearer view of variable contributions, offering practical value for climate-driven irrigation pressure assessment in semi-arid environments.

Keywords

Hybrid Deep Learning , Atmospheric Irrigation Potential Forecasting , LSTM–XGBoost , NASA Power Data

References

• Achite, M., Kumar, M., Elshaboury, N., Srivastava, A., Elbeltagi, A., & Salem, A. (2025). Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment. Scientific Reports, 15(1), 20179. https://doi.org/10.1038/s41598-025-05985-z
• Ahmed, A. A. M., Deo, R. C., Feng, Q., Ghahramani, A., Raj, N., Yin, Z., & Yang, L. (2022). Hybrid deep learning method for a week-ahead evapotranspiration forecasting. Stochastic Environmental Research and Risk Assessment, 36(3), 831–849. https://doi.org/10.1007/s00477-021-02078-x
• Allen, R. G., Pereira, L. S., Raes, D., Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
• Babaeian, E., Paheding, S., Siddique, N., Devabhaktuni, V. K., & Tuller, M. (2022). Short- and mid-term forecasts of actual evapotranspiration with deep learning. Journal of Hydrology, 612, 128078. https://doi.org/10.1016/j.jhydrol.2022.128078
• El Bilali, A., Hadri, A., Taleb, A., Tanarhte, M., EL Khalki, E. M., & Kharrou, M. H. (2025). A novel hybrid modeling approach based on empirical methods, PSO, XGBoost, and multiple GCMs for forecasting long-term reference evapotranspiration in a data scarce-area. Computers and Electronics in Agriculture, 232, 110106. https://doi.org/10.1016/j.compag.2025.110106
• Farooque, A. A., Afzaal, H., Abbas, F., Bos, M., Maqsood, J., Wang, X., & Hussain, N. (2022). Forecasting daily evapotranspiration using artificial neural networks for sustainable irrigation scheduling. Irrigation Science, 40(1), 55–69. https://doi.org/10.1007/s00271-021-00751-1
• Garofalo, S. Pietro, Ardito, F., Sanitate, N., De Carolis, G., Ruggieri, S., Giannico, V., Rana, G., & Ferrara, R. M. (2025). Robustness of Actual Evapotranspiration Predicted by Random Forest Model Integrating Remote Sensing and Meteorological Information: Case of Watermelon (Citrullus lanatus, (Thunb.) Matsum. Nakai, 1916). Water, 17(3), 323. https://doi.org/10.3390/w17030323
• Jayasinghe, W. J. M. L. P., Deo, R. C., Ghahramani, A., Ghimire, S., & Raj, N. (2021). Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated with the Boruta-Random Forest Algorithm. IEEE Access, 9, 166695–166708. https://doi.org/10.1109/ACCESS.2021.3135362
• Jia, W., Zhang, Y., Wei, Z., Zheng, Z., & Xie, P. (2023). Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model. PLOS ONE, 18(4), e0281478. https://doi.org/10.1371/journal.pone.0281478
• Li, M., Zhou, Q., Han, X., & Lv, P. (2024). Prediction of reference crop evapotranspiration based on improved convolutional neural network (CNN) and long short-term memory network (LSTM) models in Northeast China. Journal of Hydrology, 645, 132223. https://doi.org/10.1016/j.jhydrol.2024.132223
• Lu, X., Fan, J., LifengWu, & Dong, J. (2020). Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with GreyWolf Optimization Algorithm. Computer Modeling in Engineering & Sciences, 125(2), 699–723. https://doi.org/10.32604/cmes.2020.011004
• Malik, A., Jamei, M., Ali, M., Prasad, R., Karbasi, M., & Yaseen, Z. M. (2022). Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection. Agricultural Water Management, 272, 107812. https://doi.org/10.1016/j.agwat.2022.107812
• MGM. Meteoroloji Genel Müdürlüğü. (2025). İl ve İlçeler İstatistikleri – Ankara. https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=undefined&m=ANKARA
• Mokhtar, A., Al-Ansari, N., El-Ssawy, W., Graf, R., Aghelpour, P., He, H., Hafez, S. M., & Abuarab, M. (2023). Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region. Water Resources Management, 37(4), 1557–1580. https://doi.org/10.1007/s11269-023-03443-x
• NASA Langley Research Center. (2025). Prediction of Worldwide Energy Resources (POWER) – Data Access Viewer. https://power.larc.nasa.gov/data-access-viewer/
• Pu, Z., Yan, J., Chen, L., Li, Z., Tian, W., Tao, T., & Xin, K. (2023). A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting. Frontiers of Environmental Science & Engineering, 17(2), 22. https://doi.org/10.1007/s11783-023-1622-3