Modelling the rainfall–runoff data of susurluk basin

In this study, rainfall–runoff relationship was tried to be set up by using Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Interference Systems (ANFIS) models at Flow Observation Stations (FOS) on seven streams where runoff measurement has been made for long years in Susurluk Basin. A part of runoff data was used for training of ANN and ANFIS models and the other part was used to test the performance of the models. The performance comparison of the models was made with decisiveness coefficient (R2) and Root Mean Squared Errors (RMSE) values. In addition to this, a comparison of ANN and ANFIS with traditional methods was made by setting up Multi-regressional (MR) model. Except some stations, acceptable results such as R2 value for ANN model and R2 value for ANFIS model were obtained as 0.7587 and 0.8005, respectively. The high values of predicted errors, belonging to peak values at stations where multi variable flow is seen, affected R2 and RMSE values negatively.