Comparing the Performance of Deep Learning and Machine Learning Methods in Predicting Dissolved Oxygen Content

Document Type : Research Paper


1 Professor, Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

2 M.Sc. of Water and Hydraulic Structures, Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran


Water quality forecasting plays an important role in environmental monitoring, ecosystem sustainability and aquaculture. Traditional forecasting methods cannot show the non-linearity and instability of water quality well. In the present study, the water quality parameter of dissolved oxygen was modeled using intelligent Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) methods on three consecutive stations on Savanah River located in USA. For this purpose, six different flow hydraulic and hydrological parameters including water temperature, turbidity, discharge, mean water velocity, pH and specific conductivity were used daily for seven years (2021-2015) as input parameters to model dissolved oxygen. The results showed the complete superiority of the deep learning method over the machine learning methods. According to the results, the long short-term memory method for the last model, which included all parameters, in the third station with correlation coefficient, coefficient of determination and root mean square error, respectively R = 0.981, DC = 0.956 and RMSE = 0.034 for test data performed better. Finally, by performing sensitivity analysis, by removing the water temperature parameter, it was found that DC evaluation criteria decreased by 14% and RMSE increased by 100%. Therefore, water temperature was introduced as the most influential parameter in predicting dissolved oxygen in water.


Adamowski, J., Adamowski, K., & Bougadis, J. (2010). Influence of Trend on Short Duration Design Storms. Water Resources Management, 24(3), 401-413.
Ahmed, A. A. M. (2017). Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs). Journal of King Saud University - Engineering Sciences, 29(2), 151-158.
Aizenberg, I., Aizenberg, N. N., & Vandewalle, J. P. L. (2000). Multi-valued and universal binary neurons: Theory, learning and applications. Springer Science & Business Media
Antanasijević, D., Pocajt, V., Povrenović, D., Perić-Grujić, A., & Ristić, M. (2013). Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environmental Science and Pollution Research, 20(12), 9006-9013.
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166.
Chen, M., Pennington, J., & Schoenholz, S. (2018, 2018). Dynamical isometry and a mean field theory of RNNs: Gating enables signal propagation in recurrent neural networks.
Chung, C.-C., Chen, H.-H., & Ting, C.-H. (2010). Grey prediction fuzzy control for pH processes in the food industry. Journal of Food Engineering, 96(4), 575-582.
Csábrági, A., Molnár, S., Tanos, P., & Kovács, J. (2017). Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube. Ecological Engineering, 100, 63-72.
Elhatip, H., & Kömür, M. (2008). Evaluation of water quality parameters for the Mamasin dam in Aksaray City in the central Anatolian part of Turkey by means of artificial neural networks. Environmental Geology, 53, 1157-1164.
Emamgholizadeh, S., Kashi, H., Marofpoor, I., & Zalaghi, E. (2014). Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. International Journal of Environmental Science and Technology, 11(3), 645-655.
Fang, X., & Yuan, Z. (2019). Performance enhancing techniques for deep learning models in time series forecasting. Engineering Applications of Artificial Intelligence, 85, 533-542.
Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks, 18(5-6), 602-610.
Haidary, A., Amiri, B. J., Adamowski, J., Fohrer, N., & Nakane, K. (2013). Assessing the impacts of four land use types on the water quality of wetlands in Japan. Water Resources Management, 27(7), 2217-2229.
Han, D., Chan, L., & Zhu, N. (2007). Flood forecasting using support vector machines. Journal of hydroinformatics, 9(4), 267-276.
Heddam, S. (2014). Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. (1573-2959 (Electronic)).
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-term Memory. Neural computation, 9, 1735-1780.
Huan, J., Cao, W., & Qin, Y. (2018). Prediction of dissolved oxygen in aquaculture based on EEMD and LSSVM optimized by the Bayesian evidence framework. Computers and Electronics in Agriculture, 150, 257-265.
Huan, J., & Liu, X. (2016). Dissolved oxygen prediction in water based on K-means clustering and ELM neural network for aquaculture. Transactions of the Chinese Society of Agricultural Engineering, 32(17), 174-181.
Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert systems with applications, 37(2), 1784-1789.
Khotimah, W. N. (2015). Aquaculture water quality prediction using smooth SVM. IPTEK Journal of Proceedings Series, 1(1).
Li, X., & Song, J. (2020, 2015). A New ANN-Markov chain methodology for water quality prediction.
Liu, S., Xu, L., Jiang, Y., Li, D., Chen, Y., & Li, Z. (2014). A hybrid WA–CPSO-LSSVR model for dissolved oxygen content prediction in crab culture. Engineering Applications of Artificial Intelligence, 29, 114-124.
Liu, S., Xu, L., Li, D., & Zeng, L. (2012). Dissolved oxygen prediction model of eriocheir sinensis culture based on least squares support vector regression optimized by ant colony algorithm. Transactions of the Chinese Society of Agricultural Engineering, 28(23), 167-175.
Najah, A., El-Shafie, A., Karim, O. A., & El-Shafie, A. H. (2013). Application of artificial neural networks for water quality prediction. Neural Computing and Applications, 22(1), 187-201.
Niroobakhsh, M. (2012). Prediction of water quality parameter in Jajrood River basin: Application of multi layer perceptron (MLP) perceptron and radial basis function networks of artificial neural networks (ANNs). AFRICAN JOURNAL OF AGRICULTURAL RESEEARCH, 7.
Ömer Faruk, D. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), 586-594.
Pal, M., & Deswal, S. (2010). Modelling pile capacity using Gaussian process regression. Computers and Geotechnics - COMPUT GEOTECH, 37, 942-947.
Palani, S., Liong, S.-Y., Tkalich, P., & Palanichamy, J. (2009). Development of a neural network model for dissolved oxygen in seawater. Indian Journal of Marine Sciences, 38.
Pelletier, G., Chapra, S., & Tao, H. (2006). QUAL2Kw — A Framework for Modeling Water Quality in Streams and Rivers Using a Genetic Algorithm for Calibration. Environmental Modelling and Software, 419-425.
Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., & Cottrell, G. (2017). A dual-stage attention-based recurrent neural network for time series prediction. arXiv preprint arXiv:1704.02971.
Rasmussen, C. E. (2004, 2003). Gaussian processes in machine learning.
Roushangar, k., & Chamani, m. (2020). Prediction of River Discharge and Assessment its Relationship at Consecutive Hydrometric Stations Using GPR-EEMD Combined Techniques (Case Study: Housatonic River). Iranian Journal of Soil and Water Research, 50(10), 2473-2485.
Roushangar, K., & Joulazadeh, S. (2022). Investigation of the Effects of Hydraulic and Sedimentary Parameters on the Rate of Bed Load Transfport Using Statistical Correlations and Machine Learning Methods. Iranian Journal of Soil and Water Research, 53(1), 99-112.
Roushangar, K., & Shahnazi, S. (2019). Evaluating the Performance of Data-Driven Methods for Prediction of Total Sediment Load in Gravel-Bed Rivers [Article]. IRANIAN JOURNAL OF SOIL AND WATER RESEARCH, 50(6 #p00814), 1467-1477.
Sadeghi, H., Sohrabi Vafa, H., & Nouri, F. (2013). Applications of Neural Network Based on Genetic Algorithm for Long Term Energy Demand Forecasting. Quarterly Journal of Applied Theories of Economics, 1(2), 29-52.
Sagheer, A., & Kotb, M. (2019). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203-213.
Shi, P., Li, G., Yuan, Y., Huang, G., & Kuang, L. (2019). Prediction of dissolved oxygen content in aquaculture using Clustering-based Softplus Extreme Learning Machine. Computers and Electronics in Agriculture, 157, 329-338.
Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality—a case study. Ecological modelling, 220(6), 888-895.
Soyupak, S., Karaer, F., Gürbüz, H., Kivrak, E., Sentürk, E., & Yazici, A. (2003). A neural network-based approach for calculating dissolved oxygen profiles in reservoirs. Neural Computing & Applications, 12(3), 166-172.
Sun, M., Hassan, S. G., & Li, D. (2016). Models for estimating feed intake in aquaculture: A review. Computers and Electronics in Agriculture, 127, 425-438.
U.S. Geological Survey. (2020). National Water Information System data available on the World Wide Web (USGS Water Data for the Nation), accessed January 10, 2020, at URL
Vapnik, V. N. (1995). The nature of statistical learning theory. Springer-Verlag New York, Inc.
Wool, T., Ambrose, R., Martin, J., & Comer, A. (2020). WASP 8: The Next Generation in the 50-year Evolution of USEPA’s Water Quality Model. Water, 12, 1398.
Xue, H., Wang, L., & Li, D. (2013, 2013//). Design and Development of Dissolved Oxygen Real-Time Prediction and Early Warning System for Brocaded Carp Aquaculture. Computer and Computing Technologies in Agriculture VI, Berlin, Heidelberg.
Yang, D., Zhang, X., Pan, R., Wang, Y., & Chen, Z. (2018). A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. Journal of Power Sources, 384, 387-395.