Comparative study of ANN, ANFIS and AR model for daily runoff time series prediction
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Abstract:
Hydrological prediction is an important aspect of hydrology’s service for economic and society. The prediction result not only provides decision support for reservoir generation operation, but also is of great significance to the economical operation of hydropower systems, navigation, flood control and so on. The autoregressive model (AR model), artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) have been widely applied in the daily runoff time series prediction. In this paper, these three models were applied in daily runoff prediction at Tongzilin station. Nash–Sutcliffe efficiency coefficient (NS coefficient), root mean square error (RMSE) and mean absolute relative error (MARE) were used to evaluate the performances of three models. Threshold statistics index was used to analyze prediction error distribution of three models. At the same time, the prediction ability of three models was studied by gradually increasing the prediction period. The results showed that ANFIS had not only better simulation ability and generalization ability, but also better model performance in the same prediction period compared to ANN and AR model. As a result, ANFIS can be a recommended prediction model for daily runoff time series.
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