Percorrer por autor "Meziani, Assia"
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- Machine learning models for simulating daily reference evapotranspiration in a semi-arid environment using four meteorological variables: A multi-station study in Northwestern Algeria (Tlemcen Region)Publication . Meziani, Assia; Duarte, A.C.In this study, we evaluated the use of five different ML algorithms (CatBoost, XGBoost, random forest, gradient boosting, and support vector regression [SVR]) to estimate daily ET0 based only on four independent variables: 2 m air temperature, vapor pressure deficit, 10 m wind speed, and sunshine duration. We used a total of 9132 daily values (2000–2025) from the Open-Meteo Historical Weather API (2000–2025) at 10 stations in the Tlemcen province of northwest Algeria. The dataset was divided into training, validation, and testing sets using a chronological split of 70/15/15. We estimated the performance of each algorithm by using several statistics (RMSE, MAE, R2, NSE, RSR, andWillmott Index) as well as some statistics to evaluate the potential of overfitting and the ability to reproduce the behavior observed during the training phase. CatBoost had the highest overall accuracy and the most generalized performance, with an RMSE of approximately 0.292 mm day−1, MAE of approximately 0.208 mm day−1, R2 of 0.971, and NSE of 0.971 in the test set, suggesting an extremely low risk of overfitting. The optimal CatBoost model was also used to estimate the spatial and temporal variations of monthly ET0. The results showed high interannual variability (changes from year to year from −12.815 to +8.707 mm month−1) in the semi-arid region of Tlemcen but no significant long-term trends (cumulative net change of approximately −0.021 mm month−1 over 2000–2025). Therefore, the use of CatBoost is recommended as a robust, efficient, and reliable emulator of the FAO-56 Penman–Monteith equation (ET0) for estimating ET0 in semi-arid environments with limited climate data availability, and could be particularly useful in northwestern Algeria and other semi-arid Mediterranean regions.
- Neural network approximation based on ANFIS and Geographic Information System mapping for reliable evapotranspiration prediction in Khenchela, AlgeriaPublication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, AbderahamaneAccurate estimation of reference evapotranspiration (ET0) is critical for sustainable water resource management, irrigation scheduling, and climate adaptation in heterogeneous semi-arid regions. This study presents a streamlined neural network (NN) approximation inspired by the Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting daily ET0 in Khenchela province, northeastern Algeria. Utilizing meteorological and soil data from 2000 to 2024 at 16 representative stations (Babar (1), Babar (2), Babar (3), Baghai, Bouhmama, Chechar, Djellal, El Hamma, Kais, Khenchela, Khirane, M’sara, Remila, Tamza, Taouzient, and Zaoui), sourced from the Open-Meteo Historical Weather API, the model employs inputs including air temperature, relative humidity, precipitation, wind speed, sunshine duration, terrestrial radiation, soil temperature, and soil moisture. The NN was trained to closely approximate the FAO-56 Penman-Monteith reference ET0 values computed directly by the API. Performance evaluation yielded strong agreement across stations: R2 > 0.96, RMSE 0.22 - 0.46 mm/day, NSE > 0.95, RSR < 0.13, and Willmott’s index 0.88 - 0.93, with peak accuracy (R2 > 0.99, RMSE < 0.24 mm/day) at high-elevation sites. Spatial patterns, mapped via GIS-based inverse distance weighting interpolation, revealed pronounced topographic and aridity-driven variability, confirmed by Emberger and De Martonne indices. This computationally efficient NN offers a scalable surrogate for FAO-56 calculations in data-limited, heterogeneous environments, supporting precision irrigation, drought monitoring, and adaptive strategies in semi-arid North Africa and Mediterranean regions.
- Predictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar Region (Algeria)Publication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.An accurate estimation of the radiation is a prerequisite for the net radiation balance, evapotranspiration modeling, and optimal scheduling of water extraction by solar-powered irrigation, especially in the water-scarce Saharan zone. In this study, we developed a predictive model to estimate daily terrestrial radiation at the surface of the Adrar region in Algeria. We used a training set of 25 years of data (2000–2025) from 10 stations (Adrar, Tamantit, Sidi Ahmed Timmi, Fenoughil, Zaouiet Kounta, Reggane, Gharmianou, Tittaf, Ikiss, Kassbet Lahrar) with only three features: soil temperature (0–7 cm), air temperature (2 m), and vapor pressure deficit. The robustness of the models was ensured by a time-based split. The random forest (RF), gradient boosting (GB), and extra trees (ET) tree-based models were evaluated on the generalization set. RF and ET exhibited the best performance (R = 0.92, rootean square error—RMSE = 31.85 W/m2, Nash–Sutcliffe efficiency—NSE = 0.84 for RF; R = 0.92, RMSE = 32.33 W/m2, NSE = 0.84 for ET), whereas GB exhibited poor performance (R = 0.90, RMSE = 36.67 W/m2, NSE = 0.79). Finally, we proposed a map for solar-powered irrigation optimization. In particular, we demonstrated that the southern part of the Adrar region (Reggane, Zaouiet Kounta) has high potential for solar-powered irrigation (more than 350 W/m2). This study contributes to hyper-arid agricultural regions in Algeria through water conservation and the utilization of renewable energy.
- Spatiotemporal analysis and machine learning prediction of reference evapotranspiration in Khenchela, Algeria: Comparison of MLR, GRNN, and LSTM modelsPublication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, AbderahamaneReference evapotranspiration (ET₀) is a key parameter for water management in semi-arid regions with variable climates. This study analyzed the spatiotemporal dynamics of annual ET₀ in the Khenchela region of north-eastern Algeria (2000–2024). ET₀ was computed using the FAO-56 Penman–Monteith (PM) method. Spatial patterns were mapped using Inverse Distance Weighting (IDW). Meteorological data from 16 stations were used to train three models: Multiple Linear Regression (MLR), Generalized Regression Neural Network (GRNN), and Long Short-Term Memory (LSTM) to predict ET₀. The regional mean annual ET₀ increased by 7.2% from 2010 to 2019 decadal average (1 490 mm/year) to the 2020-2024 period (1597 mm/year), contributing to a cumulative 25-year increase of 7% from 2000 to 2009 baseline with hotspots in Babar 2 reaching ~2194 mm/year. The Mann–Kendall test confirmed significant upward trends (p < 0.05) driven by rising temperatures and declining relative humidity. All models performed well (R² > 0.965, RMSE < 0.49 mm/day, RSR < 0.20), with LSTM showing superior accuracy (R² > 0.987, RMSE < 0.232 mm/day, NSE ≈ 0.991, WI > 0.909). The superior performance of LSTM is attributed to its inherent capability to capture temporal autocorrelation and long-term dependencies in climatic time-series data. These findings support adaptive irrigation and drought mitigation in semi-arid regions of northern Africa.