In this study, the data at Idenak hydrometric station were used to predict the inflow to Maroun Dam reservoir. For this purpose, different models such as artificial intelligence, Holt-Winters and hybrid models were used. Partial mutual information algorithm was used to determine the input parameters affecting modeling the monthly inflow by artificial intelligence models. According to the Hempel and Akaike information criterion, we introduced the monthly inflow with a 3-month lag, and the temperature with a 1-month lag, with respect to the lowest values of Akaike and the highest values of Hempel as input parameters of artificial intelligence models. The results showed the weak performance of the Holt-Winters model compared to other models and confirmed the superiority of the Holt-adaptive network-based fuzzy inference system (ANFIS) hybrid model with the root-mean-square error of 54 and the coefficient of determination (R²) of 0.83 in the testing process compared to other mentioned models. In addition, the above hybrid models performed better than other models in the test process.

在这项研究中，Idenak 水文站的数据用于预测 Maroun 大坝水库的流入量。为此，使用了不同的模型，例如人工智能、Holt-Winters 和混合模型。部分互信息算法用于确定影响人工智能模型月流入量建模的输入参数。根据Hempel和Akaike信息准则，我们引入了滞后3个月的月流入量和滞后1个月的温度，相对于Akaike的最低值和Hempel的最高值作为人工输入参数智能模型。与其他提及的模型相比，测试过程中的R ^{2 ) 为 0.83。}此外，上述混合模型在测试过程中表现优于其他模型。