Haznedar, BulentKılınç, Hüseyin Çağan2025-03-262025-03-2620220920-47411573-165010.1007/s11269-022-03280-42-s2.0-85135615272https://doi.org/10.1007/s11269-022-03280-4https://hdl.handle.net/20.500.14704/1013River flow modeling plays a leading role in the management of water resources and ensuring sustainability. The complex nature of hydrological systems and the difficulty in the application process have led researchers to seek more instantaneous methods for flow predictions. Therefore, with the development of artificial intelligence-based techniques, hybrid modeling has become popular among hydrologists in recent years. For that reason, this study seeks to develop a hybrid model that integrates an adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) to predict river flow. Fundamentally, the performance of an ANFIS model depends on the optimum model parameters. Thus, it is aimed to increase the prediction performance by optimizing the ANFIS parameters with the population-based GA algorithm, which is a powerful algorithm. In this respect, the data gathered from Zamanti and Korkun Flow Measurement Stations (FMS) of Seyhan River, one of Turkey's significant rivers, were employed. Besides, the proposed hybrid ANFIS-GA approach was compared to classical ANFIS model to demonstrate the improvement of its performance. Also, within the scope of simulation studies, the traditional artificial neural networks (ANN) and the long-short term memory (LSTM) method which is a quite popular in recent years were used to predict streamflow data. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R-2 statistical metrics. In a nutshell, the outcomes indicated that the proposed ANFIS-GA method was the most successful model by achieving the highest values of R-2 (approximate to 0.9409) and R-2 (approximate to 0.9263) for the Zamanti and Korkun FMS data.eninfo:eu-repo/semantics/closedAccessANFIS; Genetic algorithm; Soft computing; Streamflow; Forecasting; River flowArticle484212Q1481936WOS:000836827600001Q1