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

Salar Valizadeh Moghadam, Ahmad Sharafati*, Hajar Feizi, Seyed Mohammad Saeid Marjaie, Seyed Babak Haji Seyed Asadollah, Davide Motta

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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 ([Formula: see text]) as the most accurate among the three models considered, highlighting the potential of deep learning approaches for water quality parameter prediction.
Original languageEnglish
Article number798
Number of pages18
JournalEnvironmental Monitoring and Assessment
Volume193
Issue number12
Early online date13 Nov 2021
DOIs
Publication statusPublished - Dec 2021

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