TY - JOUR
T1 - An efficient strategy for predicting river dissolved oxygen concentration
T2 - application of deep recurrent neural network model
AU - Moghadam, Salar Valizadeh
AU - Sharafati, Ahmad
AU - Feizi, Hajar
AU - Marjaie, Seyed Mohammad Saeid
AU - Asadollah, Seyed Babak Haji Seyed
AU - Motta, Davide
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Predictive algorithm
KW - Dissolved oxygen concentration
KW - Deep recurrent neural network
KW - Support vector machine
KW - River water quality
KW - Neural Networks, Computer
KW - Environmental Monitoring
KW - Artificial Intelligence
KW - Oxygen/analysis
KW - Rivers
UR - http://www.scopus.com/inward/record.url?scp=85119043291&partnerID=8YFLogxK
U2 - 10.1007/s10661-021-09586-x
DO - 10.1007/s10661-021-09586-x
M3 - Article
C2 - 34773156
VL - 193
JO - Environmental Monitoring and Assessment
JF - Environmental Monitoring and Assessment
SN - 0167-6369
IS - 12
M1 - 798
ER -