Suspended Sediment Forecasting by Artificial Neural Networks Using Hydro Meteorological Data

Abstract

Estimates of sediment yield are required in a wide spectrum of water resources engineering problems. The non-linear nature of suspended sediment time series necessitates the utilization of non-linear methods for the forecasting study. In this study artificial neural networks, a well known non-linear method, are employed to forecast the daily total suspended sediment amount on rivers. The neural networks are trained using the rainfall data, recorded on the river catchment, the river flow and suspended sediment data belonging to Juniata Catchment in USA. The simulations provided satisfactory forecasts in terms of the selected performance criteria comparing well with conventional methods.