The reference evapotranspiration (ET₀) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET₀ using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET₀ time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET₀ prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET₀ prediction in Serbia is the novelty of this study.

参考蒸散量（ET ₀）尤其在农业用水管理和灌溉水资源规划中发挥着重要作用。它可以使用不同的经验方程式进行计算，并可以通过应用各种人工智能技术进行预测。机器学习技术的仿真结果取决于其结构和模型输入。这项研究的目的是调查使用模型输入的最佳时滞集对每月ET ₀的预测准确性的影响使用人工神经网络（ANN）。为此，从塞尔维亚的六个气象站收集了1980-2010年期间的气象数据时间序列，即最低和最高气温，蒸气压，日照时间和风速。将三个ANN模型应用于每月ET ₀时间序列，以研究使用最佳时间滞后输入时间序列对ANN模型性能的影响。拟合优度统计的结果批准了通过测试集散点图获得的结果-使用更多的时滞是基于它们与数据集的相关性而选择的，对于每月ET _0而言更有效预言。人们认识到，所有开发的模型在Loznica和Vranje站均表现出最佳性能，而在Nis站则表现最差。同时评估使用不同数量的时滞和时滞集对输入时间序列与数据集的相关性更强的影响对塞尔维亚每月ET ₀预测中ANN模型性能的影响是新颖的这项研究。