For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ET_o) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ET_o at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (R_s), wind speed (U_s), relative humidity (RH), and maximum and minimum air temperatures (T_max and T_min) of 14 years (2000–2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., T_min, T_max, RH, U_s, R_s: scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ET_o in the study region.

为了有效规划灌溉计划，水预算，作物模拟和水资源管理，准确估算参考蒸散量（ET _o）是必不可少的。在当前研究中，采用混合支持向量回归（SVR）与鲸鱼优化算法（SVR-WOA）来估计在三种不同的最佳输入情况下位于阿尔及利亚北部的阿尔及尔和特莱姆森气象站的每月ET _o。每月气候参数，即太阳辐射（R _s），风速（U _s），相对湿度（RH）以及最高和最低气温（T _max和T _min从这两个站获得了14年（2000-2013年）的）。通过性能指标，即平均绝对误差（MAE），均方根值，均方根误差，均方根误差，均方根误差，均方根误差，均方根误差，均方根误差，均方根误差，均方根误差，均方根误差，均方根误差，均方根平方误差（RMSE），散射指数（IOS），一致性指数（IOA），皮尔逊相关系数（PCC），纳什-苏克利夫效率（NSE）和图形解释（时变和散布图，雷达图和泰勒图）。结果表明，在所有情况下，两个站点的SVR-WOA模型均优于SVR-MVO和SVR-ALO模型。具有5个输入（即T _min， T _max， RH，U _s和R _s）的SVR-WOA-1模型：方案1）的MAE最低值= 0.0658 / 0.0489 mm /月，RMSE = 0.0808 / 0.0617 mm /月，IOS = 0.0259 / 0.0165，NSE最高值= 0.9949 / 0.9989，PCC = 0.9975 / 0.9995 ，两个站的测试周期的IOA分别为0.9987 / 0.9997。与用于估计研究区域每月ET _o的SVR-MVO和SVR-ALO模型相比，建议的混合SVR-WOA模型被发现更合适，更有效。