Abstract:

As far as a floodprone 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 datadriven techniques. In the present study,a relative novel black box technology,namely adaptivenetworkbased 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 informationbased 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.

Key words: flood forecasting model, adaptive network, fuzzy inference system