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Percorrer por autor "Khechekhouche, Abderahamane"

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  • Neural network approximation based on ANFIS and Geographic Information System mapping for reliable evapotranspiration prediction in Khenchela, Algeria
    Publication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, Abderahamane
    Accurate 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.
    2026Artigo de investigação Acesso aberto Ver mais
  • Spatiotemporal analysis and machine learning prediction of reference evapotranspiration in Khenchela, Algeria: Comparison of MLR, GRNN, and LSTM models
    Publication . Meziani, Assia; Mega, Nabil; Miloudi, Abdelmonen; Duarte, A.C.; Khechekhouche, Abderahamane
    Reference 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.
    2026Artigo de investigação Acesso aberto Ver mais