Abstract:
This paper demonstrates the estimation and simulation of discharge and sediment concentration for two river basins
in the USA and India. The first-order Sugeno fuzzy inference system was utilised to model the stage, discharge and
sediment concentration relationship. A subtractive clustering algorithm, along with a least-squares estimation, was
used to generate the fuzzy rules that describe the relationship between input and output data of stage, discharge
and sediment concentration, which change over time. The fuzzy rules were tuned by a back-propagation algorithm.
The results are illustrated using simulation and virtual reality. A comparison was made between the estimates
provided by the neuro-fuzzy model and a multi-linear regression model. Different statistical criteria were used to
evaluate the performance of both models in estimating discharge and sediment concentration. Comparison of the
results reveals that, in general, the neuro-fuzzy model gives better estimates than the multi-linear regression model
in terms of root mean square and sum of squares errors. Furthermore, compared with the multi-linear regression
model, the neuro-fuzzy model yields statistical properties of estimates that are closer to actual historical data.