Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning

Talei A., Chua L.H.C., Quek C., Jansson P-E., Journal of Hydrology, Vol 488, pp. 17-32., 2013

A study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall–runoff modeling
application. The local learning model was first tested on three different catchments: an outdoor experimental
catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment
2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local
learning model were comparable or better than results obtained from physically-based, i.e. Kinematic
Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning
(HBV) model. The local learning algorithm also required a shorter training time compared to a
global learning NFS model. The local learning model was next tested in real-time mode. The real-time implementation of the local learning model gave better results, without the need for retraining, When compared to a batch NFS model, where it was found that the batch had to be retrained periodically in order to achieve similar results.