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An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model

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Abstract

Dissolved oxygen (DO) concentration in water is one of the key parameters for assessing river water quality. Artificial intelligence (AI) methods have previously proved to be accurate tools for DO concentration prediction. This study presents the implementation of a deep learning approach applied to a recurrent neural network (RNN) algorithm. The proposed deep recurrent neural network (DRNN) model is compared with support vector machine (SVM) and artificial neural network (ANN) models, formerly shown to be robust AI algorithms. The Fanno Creek in Oregon (USA) is selected as a case study and daily values of water temperature, specific conductance, streamflow discharge, pH, and DO concentration are used as input variables to predict DO concentration for three different lead times (“t + 1,” “t + 3,” and “t + 7”). Based on Pearson’s correlation coefficient, several input variable combinations are formed and used for prediction. The model prediction performance is evaluated using various indices such as correlation coefficient, Nash–Sutcliffe efficiency, root mean square error, and mean absolute error. The results identify the DRNN model (\({{CC}_{Testing}= 0.97, NSE}_{Testing}=0.948, {RMSE}_{Testing}=0.43\mathrm{ and }{MAE}_{Testing}=0.25\)) as the most accurate among the three models considered, highlighting the potential of deep learning approaches for water quality parameter prediction.

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Authors and Affiliations

  1. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Salar Valizadeh Moghadam, Ahmad Sharafati, Seyed Mohammad Saeid Marjaie & Seyed Babak Haji Seyed Asadollah

  2. Department of Water Engineering, Faculty of Agriculture, Tabriz University, Tabriz, Iran

    Hajar Feizi

  3. Department of Mechanical and Construction Engineering, Northumbria University, Wynne Jones Building, Newcastle upon Tyne, NE1 8ST, UK

    Davide Motta

Authors
  1. Salar Valizadeh Moghadam
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  2. Ahmad Sharafati
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  3. Hajar Feizi
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  4. Seyed Mohammad Saeid Marjaie
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  5. Seyed Babak Haji Seyed Asadollah
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  6. Davide Motta
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Contributions

Salar Valizadeh Moghadam carried out the investigation and participated in drafting the manuscript. Ahmad Sharafati proposed the topic and participated in coordination and paper editing. Hajar Feyzi carried out modeling and participated in drafting the manuscript. Seyed Mohammad Saeid Marjaie carried out the review analysis and participated in drafting the manuscript. Seyed Babak Haji Seyed Asadollah aided in the interpretation of results and participated in drafting the manuscript. Davide Motta carried out an investigation and paper editing. All authors read and approved the final manuscript.

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Correspondence to Ahmad Sharafati.

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Moghadam, S.V., Sharafati, A., Feizi, H. et al. An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model. Environ Monit Assess 193, 798 (2021). https://doi.org/10.1007/s10661-021-09586-x

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