The flow assessment in a river is of vital interest in hydraulic engineering for flood warning and evacuation measures. To operate water structures more efficiently, models that forecast river discharge are desired to be of high precision and certain degree of accuracy. Therefore, in this study, two artificial intelligence models, namely kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS), were applied for the monthly river flow (MRF) modeling. For this aim, Mississippi river with three consecutive hydrometric stations was selected as case study. Using the previous MRF values during the period of 1950–2019, several models were developed and tested under two scenarios (i.e. modeling based on station's own data or previous station's data). Wavelet transform (WT) and ensemble empirical mode decomposition (EEMD) as data processing approaches were used for enhancing modeling capability. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the model's capability up to 25%. It was observed that the previous station's data could be applied successfully for MRF modeling when the station's own data were not available. The best-applied model dependability was assessed via uncertainty analysis, and an allowable degree of uncertainty was found in MRF modeling.

河流中的流量评估在水利工程中对于洪水预警和疏散措施具有重要意义。为了更有效地运行水结构，预测河流流量的模型需要具有高精度和一定程度的准确度。因此，在本研究中，两种人工智能模型，即核极限学习机（KELM）和多元自适应回归样条（MARS），被应用于月河流量（MRF）建模。为此，选择了具有三个连续水文站的密西西比河作为案例研究。使用 1950-2019 年期间的先前 MRF 值，在两种情况下（即基于台站自身数据或先前台站数据建模）开发和测试了多个模型。小波变换 (WT) 和集成经验模式分解 (EEMD) 作为数据处理方法用于增强建模能力。获得的结果表明，集成模型产生了更准确的结果。数据处理将模型的能力提高了 25%。据观察，当台站自己的数据不可用时，前一个台站的数据可以成功地应用于 MRF 建模。通过不确定性分析评估了最佳应用模型的可靠性，并在 MRF 建模中发现了允许的不确定性程度。据观察，当台站自己的数据不可用时，前一个台站的数据可以成功地应用于 MRF 建模。通过不确定性分析评估了最佳应用模型的可靠性，并在 MRF 建模中发现了允许的不确定性程度。据观察，当台站自己的数据不可用时，可以成功地将先前台站的数据应用于 MRF 建模。通过不确定性分析评估了最佳应用模型的可靠性，并在 MRF 建模中发现了允许的不确定性程度。