This study investigates the potential of two evolutionary neuro-fuzzy inference systems, adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (ANFIS–PSO) and genetic algorithm (ANFIS–GA), in modelling reference evapotranspiration (ET₀). The hybrid models were tested using Nash–Sutcliffe efficiency, root mean square errors and determination coefficient (R²) statistics and compared with classical ANFIS, artificial neural networks (ANNs) and classification and regression tree (CART). Various combinations of monthly weather data of solar radiation, relative humidity, average air temperature and wind speed gotten from two stations, Antalya and Isparta, Turkey, were used as input parameters to the developed models to estimate ET₀. The recommended evolutionary neuro-fuzzy models produced better estimates compared to ANFIS, ANN and CART in modelling monthly ET₀. The ANFIS–PSO and/or ANFIS–GA improved the accuracy of ANFIS, ANN and CART by 40%, 32% and 66% for the Antalya and by 14%, 44% and 67% for the Isparta, respectively.

中文翻译：

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结合神经模糊和进化策略对参考蒸散量进行建模

这项研究调查了两个进化神经模糊推理系统（具有粒子群优化（ANFIS–PSO）和遗传算法（ANFIS–GA）的自适应神经模糊推理系统（ANFIS）在建模参考蒸散量（ET ₀）方面的潜力。使用Nash-Sutcliffe效率，均方根误差和确定系数（R ²）统计数据对混合模型进行了测试，并与经典ANFIS，人工神经网络（ANN）和分类回归树（CART）进行了比较。从土耳其安塔利亚和伊斯帕塔两个站获得的太阳辐射，相对湿度，平均气温和风速的每月天气数据的各种组合被用作已开发模型的输入参数，以估计ET ₀。与ANFIS，ANN和CART相比，在每月ET ₀建模中，推荐的进化神经模糊模型产生了更好的估计。ANFIS–PSO和/或ANFIS–GA将Antalya的ANFIS，ANN和CART的准确性分别提高了40％，32％和66％，将Isparta的准确性分别提高了14％，44％和67％。