Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74431
Title: Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models
Authors: Alizadeh, MJ
Jafari Nodoushan, E
Kalarestaghi, N
Chau, KW 
Keywords: Ensemble forecasting
Several lead times
Suspended sediment concentration
Updating input structure
Wavelet-ANN
Issue Date: 2017
Publisher: Springer
Source: Environmental science and pollution research, 2017, v. 24, no. 36, p. 28017-28025 How to cite?
Journal: Environmental science and pollution research 
Abstract: 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. For this purpose, different wavelet families are linked with the ANN model and performance of each model is evaluated using error measures. The Skagit River near Mount Vernon in Washington county is selected as the case study. The daily flow discharge and suspended sediment concentration (SSC) in the current day are considered as input variables to predict suspended sediment concentration in the next day. For more lead times, the input structure is updated by adding the forecast of SSC in the previous time step. Results of this study demonstrate that incorporating both observed and predicted variables in the input structure improves performance of conventional models in which those only employ observed time series as input variables. Moreover, ensemble model developed for each lead time outperforms the best single wavelet-ANN model which indicates superiority of the ensemble model over the other one. Findings of this study reveal that acceptable forecasts of daily suspended sediment concentration up to 3 days in advance can be achieved using the proposed methodology.
URI: http://hdl.handle.net/10397/74431
ISSN: 0944-1344
EISSN: 1614-7499
DOI: 10.1007/s11356-017-0405-4
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