Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data [An article from: Environmental Modelling and Software]

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Estimates of sediment load are required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods for simulating the suspended sediment load. In this study artificial neural networks (ANNs) are employed to estimate the daily total suspended sediment load on rivers. Two different ANN algorithms, the feed-forward back-propagation (FFBP) method and the radial basis functions (RBF), were used for this purpose. The neural networks are trained using rainfall flow and suspended sediment load data from the Juniata Catchment, USA. The simulations provided satisfactory simulations in terms of the selected performance criteria comparing well with conventional multi-linear regression. Similarly, the simulated sediment load hydrographs obtained by two ANN methods are found closer to the observed ones again compared with multi-linear regression.