چکیده:
Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to its
essential application to water resources management. Recently, artificial intelligence has gained much popularity owing to
its application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. This
study made us of adaptive neuro-fuzzy inference system (ANFIS) techniques and three artificial neural network
approaches, namely, the Feed-forward back-propagation (FFBP), radial basis function-based neural networks (RBF),
geomorphology-based artificial neural network (GANN) to predict the streamflow suspended sediment relationship. To
illustrate their applicability and efficiency,, the daily streamflow and suspended sediment data of Dalaki River station in
south of Iran were used as a case study. The obtained results were compared with the sediment rating curve (SRC) and
regression model (RM). Statistic measures (RMSE, MAE, and R2) were used to evaluate the performance of the models.
From the results, adaptive neuro-fuzzy (ANFIS) approach combined capabilities of both Artificial Neural Networks and
Fuzzy Logic and then reflected more accurate predictions of the system. The results showed that accuracy of estimations
provided by ANFIS was higher than ANN approaches, regression model and sediment rating curve. Additionally, relating
selected geomorphologic parameters as the inputs of the ANN with rainfall depth and peak runoff rate enhanced the
accuracy of runoff rate, while sediment loss predictions from the watershed and GANN model performed better than the
other ANN approaches together witj regression equations in Modeling of stream flow–suspended sediment relationship.
خلاصه ماشینی:
"ir Desert 20-2 (2015) 177-195 Modeling of streamflow- suspended sediment load relationship by adaptive neuro-fuzzy and artificial neural network approaches (Case study: Dalaki River, Iran) M.
This study made us of adaptive neuro-fuzzy inference system (ANFIS) techniques and three artificial neural network approaches, namely, the Feed-forward back-propagation (FFBP), radial basis function-based neural networks (RBF), geomorphology-based artificial neural network (GANN) to predict the streamflow suspended sediment relationship.
The adaptive neuro- fuzzy inference system (ANFIS) is a hybrid scheme that utilizes the learning capability of the artificial neural network or ANN to derive the fuzzy if-then rules with appropriate membership functions worked out from the training pairs, which in turn leads to the inference (Jang and Sun, 1995; Tay and Zhang, 1999).
The present study summarizes the recent results obtained based on the performances of an adaptive NF computing technique in daily suspended sediment prediction using the daily rainfall, streamflow and suspended sediment concentration data from Dalaki River Catchment near Persian Gulf in Iran.
The methodology of constructing the ANFIS model and three different artificial neural networks (ANN) techniques for daily suspended sediment concentration simulation and conventional multi-linear regression (MLR) and sediment rating curve (SRC) have also been presented.
Geomorphologic parameters describing the land surface drainage characteristics and surface water flow behavior were empirically associated with measured rainfall and runoff data and used as input to a three-layered back-propagation feed- forward neural network model.
4. Suspended sediment load estimation using ANFIS technique Adaptive NF model is applied as an effective approach in handling nonlinear and noisy data, especially in situations where the relationships among physical processes are not fully understood."
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