Various critical applications, spanning from watershed management to agricultural planning and ecological sustainability, hinge upon the accurate prediction of reference evapotranspiration (ET_o). In this context, our study aimed to enhance the accuracy of ET_o prediction models by combining a variety of signal decomposition techniques with an Artificial Bee Colony (ABC)–artificial neural network (ANN) (codename: ABC–ANN). To this end, historical (1979–2014) daily climate variables, including maximum temperature, minimum temperature, mean temperature, wind speed, relative humidity, solar radiation, and precipitation from four arid and semi-arid regions in Egypt: Al-Qalyubiyah, Cairo, Damietta, and Port Said, were used. Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ET_o prediction. Our results showed that the highest ET_o prediction accuracy was obtained with ABC-ANN (Train R²: 0.990 and Test R²: 0.989), (Train R²: 0.986 and Test R²: 0.986), (Train R²: 0.991 and Test R²: 0.989) and (Train R²: 0.988 and Test R²: 0.987) for Al-Qalyubiyah, Cairo, Damietta, and Port Said, respectively. The impressive results of our hybrid model attest to its importance as a powerful tool for tackling the problems associated with ET_o prediction.

从流域管理到农业规划和生态可持续性，各种关键应用都取决于对参考蒸散量 （ET_o） 的准确预测。在此背景下，我们的研究旨在通过将各种信号分解技术与人工蜂群 （ABC）-人工神经网络 （ANN）（代号：ABC-ANN）相结合来提高 ET_o 预测模型的准确性。为此，使用了埃及四个干旱和半干旱地区（Al-Qalyubiyah、Cairo、Damietta 和 Port Said）的历史（1979-2014 年）每日气候变量，包括最高温度、最低温度、平均温度、风速、相对湿度、太阳辐射和降水。采用经验模态分解、变分模态分解、集成经验模态分解、局部均值分解、自适应噪声完全集成经验模态分解和经验小波变换 6 种技术评价 ET_o 预测中的信号分解效率。我们的结果表明，使用 ABC-ANN （训练 R²：0.990 和测试 R²：0.989）、（训练 R²：0.986 和测试 R²：0.986）、（训练 R²：0.991 和测试 R²：0.989）和（训练 R²：0.988 和测试 R² ）获得最高的 ET_o 预测精度： 0.987） 分别代表 Al-Qalyubiyah、Cairo、Damietta 和 Port Said。我们的混合模型令人印象深刻的结果证明了它作为解决与 ET_o 预测相关的问题的强大工具的重要性。