Finding an accurate computational method for estimating pan evaporation (EP_m) can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called multiple model-support vector machine (MM-SVM) with the aim of showing the increasing, decreasing, and constant accuracy behavior of this hybrid model and improving the results of estimating EP compared to the two models ANN and SVM on a monthly scale of EP_m in four meteorological stations (Ardabil, Khalkhal, Manjil (from Iran), and Grand Island (from the USA)) located in semi-arid regions, using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)), was evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE), and coefficient of determination (R²)) and with the help of case visual indicators were compared. According to the results of evaluation indicators in the test phase, MM-SVM-6, ANN-5, MM-SVM-3, and MM-SVM-7 with RMSE = 1.088, 0.761, 0.829, and 0.134 mm/day; MAE = 0.79, 0.54, 0.589, and 0.105 mm/day; KGE = 0.819, 0.903, 0.972, and 0.981; and R² = 0.939, 0.962, 0.967, and 0.996 and with four input variables were introduced as the best models in Ardabil, Khalkhal, Manjil, and Grand Island stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs estimated by independent models, its power to estimate EP_m in scenarios where there is a high correlation between its components with EP_m, in a feasible state Accept to show. So that the incremental, constant, and decreasing modes in EP_m estimation accuracy by this hybrid model under the semi-arid climatic conditions of the studied areas were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.

找到一种准确的计算方法来估算蒸发皿蒸发量 (EP _m ) 有助于将这些方法应用于可持续农业系统的发展和水资源管理。在本研究中，所提出的混合方法称为多模型支持向量机 (MM-SVM)，目的是显示该混合模型的增加、减少和恒定精度行为，并与两种方法相比改善估计 EP 的结果。在 EP _m的月度规模上对 ANN 和 SVM 建模在位于半干旱地区的四个气象站（Ardabil、Khalkhal、Manjil（来自伊朗）和 Grand Island（来自美国））中，使用人工智能（AI）模型（即人工神经网络（ANN）的输出）和支持向量机（SVM）），进行了评估。使用多个统计指标（即均方根误差 (RMSE)、平均绝对误差值 (MAE)、Kling-Gupta (KGE) 和决定系数 ( R ² )）并借助 case的智能模型的结果视觉指标进行了比较。根据测试阶段评价指标结果，MM-SVM-6、ANN-5、MM-SVM-3、MM-SVM-7，RMSE=1.088、0.761、0.829、0.134 mm/day；MAE = 0.79、0.54、0.589 和 0.105 毫米/天；KGE = 0.819、0.903、0.972 和 0.981；和R²  = 0.939、0.962、0.967 和 0.996 并分别引入四个输入变量作为 Ardabil、Khalkhal、Manjil 和 Grand Island 站的最佳模型。所提出的混合模型 (MM-SVM) 能够使用其多模型策略和由独立模型估计的输入，它能够在其组件与 EP _m之间存在高度相关性的情况下，在可行状态下估计 EP _m接受显示。使得 EP _m中的递增、恒定和递减模式该混合模型在研究区半干旱气候条件下的估计精度非常明确。因此，本研究中提出的模型和优越模型的结果可以帮助当地利益相关者讨论水资源管理。