The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R² = 0.924, 0.857) and testing (R² = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region.

中文翻译：

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隐马尔可夫模型，混合支持向量机和混合人工神经模糊推理系统在水库流量预测中的比较分析（案例研究：沙特阿拉伯法赫德国坝）

对于许多与水资源管理有关的活动，例如水库运行以及洪水和干旱的控制与保护，对水库流量的精确预测具有相当重要的意义。本研究旨在开发和评估隐马尔可夫模型（HMM）和两种混合模型的适用性，即支持向量机遗传算法（SVM-GA）和人工神经模糊推理系统遗传算法（ANFIS-GA） ，用于沙特阿拉伯法赫德国王大坝的水库流入预测。基于性能评估指标和视觉检查，将HMM模型获得的结果与两个混合模型ANFIS-GA和SVM-GA的结果进行比较，并与单个SVM和ANFIS模型的结果进行比较。² = 0.924，0.857）和测试（R ² = 0.842，0.810）模型。对已开发模型的性能评估结果表明，GA引起的ANFIS和SVR预测的改善与RMSE降低约25％和Nash–Sutcliffe效率提高约13％相匹配。拟议模型的准确性令人鼓舞，证明了它们在当前半干旱地区每月流量预报中的应用潜力。