Real Time Neural Fuzzy System for Runoff Forecasting

Amin Talei, Lloyd Hock Chye Chua, ATNIER 2nd Annual International Conference on Water, 14-17 Jul 2014, Athens, Greece., 2014

Neuro-Fuzzy Systems (NFS) are computational intelligence tools that have recently been employed in hydrological modeling. The learning algorithms used are based on batch learning where all the parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, one criticism of batch learning is its inability to react to changes in the system. To solve this problem, the model must be capable of online or incremental learning. Secondly, the model should be capable of learning in real-time. To address these issues, Real Time Dynamic Evolving Neural Fuzzy Inference System (RT-DENFIS) has been developed. RT-DENFIS is a Takagi-Sugeno-type fuzzy inference system and utilizes online learning. In the present study, updating capabilities of RT-DENFIS is compared with the Adaptive Network-based Neuro-Fuzzy Inference System (ANFIS) which is a common batch (offline) NFS model for runoff forecasting in Rönne catchment, Sweden. A sub-catchment of Rönne named Klippan_2 with area of 241.3 km2 was chosen for this study. The daily discharge time series from 1961 to 2003 have been used in this study where the antecedent discharge time series up to the present day are used as inputs to predict discharge up to three days ahead. The Different performance evaluation measures including coefficient of efficiency (CE), coefficient of determination (r2), root mean square error (RMSE), mean absolute error (MAE), and relative peak estimation error (RPE) are used in this study. Results show that RT-DENFIS gave consistently better results without the need for retraining; however, the batch model (ANFIS) trained with historical data had to be retrained periodically in order to achieve similar results. Moreover, RT-DENFIS results are also compared with the results obtained by an autoregressive model with exogenous inputs (ARX) as a bench mark. It was revealed that RT-DENFIS is also superior to ARX model.