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Aly, M.S., Darwish, S.M. and Aly, A.A. (2023) High Performance Machine Learning Approach for Reference Evapotranspiration Estimation. Stochastic Environmental Research and Risk Assessment, 38, 689-713.
https://doi.org/10.1007/s00477-023-02594-y

has been cited by the following article:

• TITLE: Neural Network Approximation Based on ANFIS and Geographic Information System Mapping for Reliable Evapotranspiration Prediction in Khenchela, Algeria

  AUTHORS: Assia Meziani, Nabil Mega, Abdelmonen Miloudi, António Canatário Duarte, Abderahamane Khechekhouche

  KEYWORDS: Evapotranspiration, Neural Network, ANFIS, Modeling, Khenchela, Algeria

  JOURNAL NAME: Open Access Library Journal, Vol.13 No.3, March 5, 2026

  ABSTRACT: 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 2 > 0.99, RMSE