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

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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.

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Authors and Affiliations

  1. Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Mohamad Javad Alizadeh

  2. Department of Civil Engineering, Bijar Branch, Islamic Azad University, Bijar, Iran

    Ehsan Jafari Nodoushan

  3. School of Mathematics, Iran University of Science and Technology, Tehran, Iran

    Naghi Kalarestaghi

  4. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Kwok Wing Chau

Authors
  1. Mohamad Javad Alizadeh
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  2. Ehsan Jafari Nodoushan
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  3. Naghi Kalarestaghi
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  4. Kwok Wing Chau
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Correspondence to Mohamad Javad Alizadeh.

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Responsible editor: Philippe Garrigues

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Alizadeh, M.J., Jafari Nodoushan, E., Kalarestaghi, N. et al. Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models. Environ Sci Pollut Res 24, 28017–28025 (2017). https://doi.org/10.1007/s11356-017-0405-4

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  • DOI: https://doi.org/10.1007/s11356-017-0405-4

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