As far as a floodprone region is concerned, a rapid and accurate flood forecasting is especially significant because it can extend the lead time for issuing disaster warnings,thus allowing sufficient time for people in hazardous areas to take appropriate action,such as evacuation.Although they give a deep clairvoyance to physical mechanism of flood forming,conventional conceptual forecasting models are inconvenient for operational hydrologists in practice.Therefore,many called “black box” models based on systems theoretic techniques, such as linear regression (LR),autoregressive moving average (ARMA), and artificial neural network (ANN),are employed.Compared with conceptual models, often they can provide a rapid prediction with an accepted degree of accuracy in view of depending only on datadriven techniques. In the present study,a relative novel black box technology,namely adaptivenetworkbased fuzzy inference system (ANFIS) in which Takagi and Segeno's rule was adopted,was proposed for streamflow forecasting in the main channel of the Yangtze River.In the meantime,a linear regression (LR) model was used as the benchmark for ANFIS model evaluation.In the ANFIS model, back propagation (BP) learning algorithm and hybrid learning algorithm (Combined BP and least squared error) were applied to the model,respectively.In addition,in order to avoid overfitting of training data,a statistic informationbased data partition technique was used to split raw data into three parts:training data,testing data,and validation data.Of them,testing data played a role as early stopping,which helps obtain the optimal training epoch during addressing training data.Results showed that ANFIS model is superior to the LR model,and the optimal model is the ANFIS model with hybrid learning algorithm and trapezoidal membership functions for the present case.A further analysis revealed the powerful capability of ANFIS is due to the local linear approximation technique being employed in ANFIS model, which improve the capturing capacity for training data if the overfitting can be well controlled.