Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models.

This study explores two ideas to made an improvement on the artificial neural network (ANN)-based models for suspended sediment forecasting in several time steps ahead. In this regard, both observed and forecasted time series are incorporated as input variables of the models when applied for more than one lead time. Secondly, least-square ensemble models employing multiple wavelet-ANN models are developed to increase the performance of the single model.