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Long short-term memory for predicting daily suspended sediment concentration

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Abstract

Frequent and accurate estimation of suspended sediment concentration (SSC) in surface waters and hydraulic schemes is of prime importance for proper design, operation and management of many hydraulic projects. in the present study, a long short-term memory (LSTM) was considered for predicting daily suspended sediment concentration in a river. The LSTM extends recurrent neural network with memory cells, instead of recurrent units, to store and output information, easing the learning of temporal relationships on long time scales. To build the model, daily observed time series of river discharge (Q) and SSC in the Schuylkill River in the United States were used. The results of the proposed model were evaluated and compared with the feedforward neural network and the adaptive neuro fuzzy inference system models which were trained using three different learning algorithms and widely used in the literature for prediction of daily SSC. The comparison of prediction accuracy of the models demonstrated that the LSTM model could satisfactory predict SSC time series, and adequately estimate cumulative suspended sediment load (SSL).

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Funding

This research was not funded by any foundation.

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Authors and Affiliations

  1. Institute of Hydraulic and Water Resources Engineering, Technische Universität München, Arcisstraße 21, 80333, Munich, Germany

    Keivan Kaveh, Minh Duc Bui & Peter Rutschmann

  2. Faculty of Management, University of Tehran, Jalal Al-e-Ahmad Ave, Nasr Bridge, Tehran, Iran

    Hamid Kaveh

Authors
  1. Keivan Kaveh
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  2. Hamid Kaveh
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  3. Minh Duc Bui
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  4. Peter Rutschmann
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Contributions

KK and HK designed the study, processed and analyzed the data, developed the models, interpreted the results and wrote the paper. The study has been carried out under the supervision of MDB and PR, who contributed to the model development stage with theoretical consideration and practical guidance, assisted in the interpretations and integration of the results and helped in preparation of this paper with proof reading and corrections.

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Correspondence to Keivan Kaveh.

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Kaveh, K., Kaveh, H., Bui, M.D. et al. Long short-term memory for predicting daily suspended sediment concentration. Engineering with Computers 37, 2013–2027 (2021). https://doi.org/10.1007/s00366-019-00921-y

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