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

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 m(2) (Catchment 1), a small urban catchment 5.6 km(2) in size (Catchment 2), and a large rural watershed with area of 241.3 km(2) (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 Byrans Vattenbalan-savdelning (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, where the model was continuously adapted when presented with current information in real time. 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 model had to be retrained periodically in order to achieve similar results. (C) 2013 Elsevier B.V. All rights reserved.