Journal Article

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

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

2021

Overview

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 , N S E Testing = 0.948 , RMSE Testing = 0.43 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.

• Publisher: Springer International Publishing,Springer Nature B.V
• Subject: Accuracy / Algorithms / Artificial intelligence / Artificial neural networks / Atmospheric Protection/Air Quality Control/Air Pollution / Case studies / Coastal inlets / Correlation coefficient / Correlation coefficients / Creeks & streams / Deep learning / Dissolved oxygen / Earth and Environmental Science / Ecology / Ecotoxicology / Environment / Environmental Management / Environmental monitoring / Environmental science / Learning theory / Machine learning / Mathematical models / Monitoring/Environmental Analysis / Neural networks / Optimization / Oregon / Oxygen / Parameters / Performance evaluation / prediction / Predictions / Quality assessment / Recurrent neural networks / River water / River water quality / Rivers / Stream discharge / Stream flow / streams / Support vector machines / Water quality / Water temperature