Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning
2013 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 488, 17-32 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2013. Vol. 488, 17-32 p.
Rainfall-runoff modeling, Local learning, Global learning, Neuro-fuzzy systems, ANFIS, DENFIS
Other Environmental Engineering
IdentifiersURN: urn:nbn:se:kth:diva-123428DOI: 10.1016/j.jhydrol.2013.02.022ISI: 000318325000002ScopusID: 2-s2.0-84886101097OAI: oai:DiVA.org:kth-123428DiVA: diva2:626848
QC 201306102013-06-102013-06-102013-06-10Bibliographically approved