Application of soft computing techniques in river flow modeling in the case of Euphrates-Tigris basin

River stream estimation is a subject matter that needs constant research and development since it is all-important in the management of water resources, meeting the water demand, irrigation and agricultural activities, and providing distant signal in unwanted situations such as floods. Unfortunately, a universal technique has been impossible to talk about yet although many techniques have been used for estimation and modeling. This has made it necessary to develop different techniques and / or to make comparisons between techniques and to determine the most accurate method for the parameters used. In this context, in this study, the distribution graphs of the flow data fourteen stations located in the Euphrates-Tigris Basin for the years 1981-2010 were created and their conformity to the normal distribution was investigated Evaluation has been made with Adaptive Neural Fuzzy Logic System (ANFIS), Support Vector Regression (SVR) techniques and the newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Networks (ENN) artificial intelligence techniques. Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE) and correlation coefficient (r), which are frequently used, were used to determine the model performance. In addition, it is aimed to find out which technique gives better results with rank analysis. Although all models work well, the sequence with regards to the comparison outcomes of the techniques obtained from rank analysis was observed to be ELM, GPR, ENN, SVR, ANFIS respectively. In order to better interpret the results, Taylor diagrams and time series graphics of the model outputs were created.