Stage-discharge relations for low-gradient tidal streams using data-driven models

Development of stage-discharge relationships for coastal low-gradient streams is a challenging task. Such relationships are highly nonlinear, nonunique, and often exhibit multiple loops. Conventional parametric regression methods usually fail to model these relationships. Therefore, this study examines the utility of two data-driven computationally intensive modeling techniques namely, artificial neural networks and local nonparametric regression, to model such complex relationships. The results show an overall good performance of both modeling techniques. Both neural network and local regression models are able to predict and reproduce the stage discharge multiple loops that are observed at the outlet of a 28.5 km(2) low-gradient subcatchment in southwestern Louisiana. However, the neural network model is characterized with higher prediction ability for most of the tested runoff events. In agreement with the physical characteristics of low-gradient streams, the results indicate the importance of including information about downstream and upstream water levels, in addition to water level at the prediction site.