Dam inflow prediction is important in terms of optimal water allocation and reduction of potential risks of floods and droughts. It is necessary to select a suitable model to reduce uncertainties in long-term and short-term predictions. In this study a probabilistic model of Bayesian Networks (BNs) was used to evaluate its efficiency in predicting inflow into reservoirs considering the uncertainties. For this purpose, continuous BNs as well as integration of K-means clustering and discrete BNs were applied for predicting magnitude and range of inflows, respectively in terms of annual and monthly prediction scenarios. In this regard, the Zayandehrud Dam reservoir in Iran was selected to test this model. To achieve the best network structure in these scenarios, different patterns were defined based on the combination of predictors. According to the magnitude predictions, the MAPE and R² indicators in annual model were respectively 21% and 0.62 and in monthly model were respectively 49% and 0.71. According to the results of the inflow range prediction, the prediction accuracy of the annual and monthly patterns was 75% and 83%, respectively. Modelling results showed that BN performs better in predicting the inflow range than its numerical prediction. The proposed model can improve the decision making of reservoirs operation.

大坝入流预测对于优化配水和减少洪水和干旱的潜在风险非常重要。必须选择合适的模型以减少长期和短期预测中的不确定性。在这项研究中，使用贝叶斯网络（BNs）的概率模型来评估其在考虑不确定性的情况下预测流入水库的效率。为此，分别采用年度和每月预测方案，将连续的BN以及K-均值聚类和离散BN的集成分别用于预测流量的大小和范围。在这方面，选择了伊朗的Zayandehrud大坝水库对该模型进行测试。为了在这些情况下获得最佳的网络结构，基于预测变量的组合定义了不同的模式。年度模型中的^2个指标分别为21％和0.62，每月模型中的^2个指标分别为49％和0.71。根据入流范围预测的结果，年度和月度模式的预测准确度分别为75％和83％。建模结果表明，BN在预测入流范围方面比其数值预测更好。提出的模型可以改善水库调度决策。