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

Moghadam, Salar Valizadeh, Sharafati, Ahmad, Feizi, Hajar, Marjaie, Seyed Mohammad Saeid, Asadollah, Seyed Babak Haji Seyed and Motta, Davide (2021) An efficient strategy for predicting river dissolved oxygen concentration: application of deep recurrent neural network model. Environmental Monitoring and Assessment, 193 (12). p. 798. ISSN 0167-6369

DO_Moghadam, Sharafati, Feyzi, Seyed, Marjaie, Asadollah, Motta.pdf - Accepted Version

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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 ([Formula: see text]) as the most accurate among the three models considered, highlighting the potential of deep learning approaches for water quality parameter prediction.

Item Type: Article
Uncontrolled Keywords: River Water Quality, Dissolved Oxygen Concentration, Predictive Algorithm, Deep, Recurrent Neural Network, Artificial Neural Network, Support Vector Machine
Subjects: F800 Physical and Terrestrial Geographical and Environmental Sciences
G900 Others in Mathematical and Computing Sciences
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: John Coen
Date Deposited: 03 Dec 2021 11:19
Last Modified: 13 Nov 2022 08:00

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