Rainfall-runoff modelling with neuro-fuzzy systems - Amin Talei (2013)
Intelligent computing tools based on Fuzzy Logic and Artificial Neural Networks (ANN) have been used in hydrological modeling with promising results. A new approach of combining these two powerful artificial intelligence or AI tools, known as Neuro-Fuzzy Systems (NFS), has increasingly attracted researchers in various fields. Although many studies have been carried out using this approach in rainfall-runoff (R-R) applications, relatively few studies have been undertaken to evaluate the fundamental behavior of such models. This study employed NFS for R-R modeling and attempts to address issues related to (i) input selection for NFS in event-based R-R modeling, (ii) Training event selection for NFS in event-based R-R modeling, (iii) adaptability in NFS for R-R modeling, and (iv) interpretability in NFS for R-R modeling.