Accurate and reliable prediction of streamflow is vital to the optimization of water resources management, reservoir flood operations, catchment, and urban water management. In this research, support vector regression (SVR) was optimized by six meta-heuristic algorithms, namely, Ant Lion Optimization (SVR-ALO), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Harris Hawks Optimization (SVR-HHO), Particle Swarm Optimization (SVR-PSO), and Bayesian Optimization (SVR-BO) to predict daily streamflow in Naula watershed, State of Uttarakhand, India. The significant inputs and parameter combinations for hybrid SVR models were extracted through Gamma Test before processing. The results obtained by hybrid SVR models during calibration (training) and validation (testing) periods, which were compared against observed streamflow using performance indicators of root mean square error (RMSE), scatter index (SI), coefficient of correlation (COC), Willmott index (WI), and by visual inspection (time-series plot, scatter plot and Taylor diagram). The results of comparison demonstrated that SVR-HHO during calibration/validation periods (RMSE = 92.038/181.306 m³/s, SI = 0.401/0.715, COC = 0.881/0.717, and WI = 0.928/0.777) had superior performance to the SVR-ALO, SVR-MVO, SVR-SHO, SVR-PSO, and SVR-BO models in predicting daily streamflow in the study basin. In addition, the new HHO algorithm outperformed the other meta-heuristic algorithms in terms of prediction accuracy.

准确可靠的水流量预测对于优化水资源管理，水库洪水调度，集水区和城市水管理至关重要。在这项研究中，通过六种元启发式算法优化了支持向量回归（SVR），即蚁狮优化（SVR-ALO），多版本优化器（SVR-MVO），斑点鬣狗优化器（SVR-SHO），哈里斯Hawks优化（SVR-HHO），粒子群优化（SVR-PSO）和贝叶斯优化（SVR-BO）可以预测印度北阿坎德邦瑙拉流域的日流量。在处理之前，通过Gamma测试提取了混合SVR模型的重要输入和参数组合。在校准（训练）和验证（测试）期间，混合SVR模型获得的结果，使用均方根误差（RMSE），散点指数（SI），相关系数（COC），威尔莫特指数（WI）的性能指标与观察到的流量进行比较，并通过目视检查（时间序列图，散点图和泰勒图）。比较结果表明，SVR-HHO在校准/验证期间（RMSE = 92.038 / 181.306 m³ / s，SI = 0.401 / 0.715，COC = 0.881 / 0.717和WI = 0.928 / 0.777）具有优于SVR-ALO，SVR-MVO，SVR-SHO，SVR-PSO和SVR-BO型号的性能预测研究盆地的日流量。此外，新的HHO算法在预测准确性方面胜过其他元启发式算法。