Suspended sediment prediction using two different feed-forward back-propagation algorithms

In this paper the capability of two different feed-forward back-propagation neural network algorithms, namely Levenber-Marquardt and gradient-descent, in solving complex nonlinear problems is utilized for suspended sediment prediction. The monthly streamflow and suspended sediment data from two stations, Palu and Cayagzi, in the Firat Basin in Turkey are used as case studies. The first part of the study involves the prediction of sediment data for the two stations. The second part of the study focuses on the prediction of the downstream station sediment data using upstream data. The effect of the periodicity on model performance is also investigated in each application.