Intelligent simulation of river process using ANN and ANFIS

In the present study, artificial neural networks (ANNs), neuro-fuzzy (NF), multi linear regression (MLR) and conventional sediment rating curve (SRC) models are considered for time series modeling of suspended sediment concentration (SSC) in rivers. As for the artificial intelligence systems, feed forward back propagation (FFBP) method and Sugeno inference system are used for ANNs and NF models, respectively. The models are trained using daily river discharge and SSC data belonging to Tezerjan gauging station in Yazd province in Iran. Obtained results demonstrate that ANN and NF models are in good agreement with the observed SSC values; while they depict better results than MLR and SRC methods. For example, the determination coefficient is 0.616 for NF model, while it is 0.443, 0.216 and 0.181 for ANN, MLR and SRC models, respectively.

Keywords:

Neuro-fuzzy , Artificial neural networks , Suspended sediment prediction , Multi linear regression