Precise estimation of river flow in catchment areas has a significant role in managing water resources and, particularly, making firm decisions during flood and drought crises. In recent years, different procedures have been proposed for estimating river flow, among which hybrid artificial intelligence models have garnered notable attention. This study proposes a hybrid method, so-called support vector machine–artificial flora (SVM-AF), and compares the obtained results with outcomes of wavelet support vector machine models and Bayesian support vector machine. To estimate discharge value of the Dez river basin in the southwest of Iran, the statistical daily watering data recorded by hydrometric stations located at upstream of the dam over the years 2008–2018 were investigated. Four performance criteria of coefficient of determination (R²), root-mean-square error, mean absolute error, and Nash–Sutcliffe efficiency were employed to evaluate and compare performances of the models. Comparison of the models based on the evaluation criteria and Taylor’s diagram showed that the proposed hybrid SVM-AF with the correlation coefficient R² = 0.933–0.985, root-mean-square error RMSE = 0.008–0.088 m³/s, mean absolute error MAE = 0.004–0.040 m³/s, and Nash-Sutcliffe coefficient NS = 0.951–0.995 had the best performance in estimating daily flow of the river. The estimation results showed that the proposed hybrid SVM-AF model outperformed other models in efficiently predicting flow and daily discharge.

准确估算集水区的河流流量在管理水资源方面，尤其是在洪水和干旱危机期间做出坚定决策方面，具有重要作用。近年来，已经提出了用于估计河流量的不同程序，其中混合人工智能模型引起了广泛关注。这项研究提出了一种混合方法，即所谓的支持向量机-人工菌群（SVM-AF），并将获得的结果与小波支持向量机模型和贝叶斯支持向量机的结果进行了比较。为了估算伊朗西南部Dez河流域的排放值，调查了2008-2018年大坝上游水文站的每日统计供水数据。确定系数的四个性能标准（R ²），均方根误差，平均绝对误差和Nash-Sutcliffe效率用于评估和比较模型的性能。根据评估标准和泰勒图对模型进行的比较表明，所提出的混合SVM-AF具有相关系数R ² = 0.933–0.985，均方根误差RMSE = 0.008–0.088 m ³ / s，平均绝对误差MAE = 0.004–0.040 m ³ / s，Nash-Sutcliffe系数NS = 0.951–0.995在估算河流日流量方面表现最佳。估计结果表明，所提出的混合SVM-AF模型在有效地预测流量和日流量方面优于其他模型。