After precipitation, reference evapotranspiration (ET_O) plays a crucial role in the hydrological cycle as it quantifies water loss. ET_O significantly impacts the water balance and holds great importance at the basin level because of the spatial distribution of managing water resources. Large scale teleconnection indices (LSTIs) play a vital role by influencing climatic variables and can be pivotal in determining ET_O and its predictive variables. This study aimed to model and forecast annual ET_O in Iran’s basins by utilizing LSTIs and employing various machine learning models (MLMs) such as least squares support vector machine, generalized regression neural network, multi-linear regression (MLR), and multi-layer perceptron (MLP). Initially, climate data from 122 synoptic stations covering six and 30, main and sub basins were collected, and annual ET_O values were computed using the Food and Agriculture Organization 56 (PMF 56) Penman–Monteith equation. The correlations between these values and 37 LSTIs were examined within lead times ranging from 7 to 12 months. Through a stepwise approach, the most influential predictor indices (LSTIs) were selected as input datasets for the MLMs. The findings revealed the significant influence of factors such as carbon dioxide (CO₂), Atlantic multidecadal oscillation, Atlantic Meridional Mode, and East Atlantic on annual ET_O. Overall, all MLMs performed well in terms of the Scatter Index during both training and testing phases across all sub-basins. Furthermore, the MLP and MLR models displayed superior performance compared to other models in the training and testing evaluations based on various assessment metrics.

降水后，参考蒸散量 (ET _O ) 在水文循环中发挥着至关重要的作用，因为它可以量化水损失。由于水资源管理的空间分布，ET _O显着影响水平衡，并且在流域层面具有重要意义。大规模遥相关指数 (LSTI) 通过影响气候变量发挥着至关重要的作用，并且对于确定 ET _O及其预测变量至关重要。本研究旨在利用 LSTI 和各种机器学习模型 (MLM)（例如最小二乘支持向量机、广义回归神经网络、多元线性回归 (MLR) 和多层模型）对伊朗流域的年度 ET _O进行建模和预测。感知器（MLP）。最初，收集了覆盖 6 个和 30 个主要流域和次流域的 122 个天气站的气候数据，并使用粮食及农业组织 56 (PMF 56) Penman-Monteith 方程计算了年度 ET _O值。这些值与 37 个 LSTI 之间的相关性在 7 至 12 个月的交付周期内进行了检查。通过逐步方法，选择最有影响力的预测指数 (LSTI) 作为 MLM 的输入数据集。研究结果揭示了二氧化碳（CO ₂ ）、大西洋数十年振荡、大西洋经向模态和东大西洋等因素对年ET _O的显着影响。总体而言，在所有子流域的训练和测试阶段，所有传销在分散指数方面都表现良好。此外，在基于各种评估指标的训练和测试评估中，MLP 和 MLR 模型比其他模型表现出优越的性能。