Dissolved Oxygen Modeling Using Deep Learning and Pre-Processor Methods

Document Type : Research Paper

Authors

Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.

Abstract

Water pollution is a major global problem that requires constant evaluation and revision of water resources policy at all levels. Dissolved oxygen (DO) is one of the most important indicators of water quality. In the present study, the water quality parameter of dissolved oxygen using intelligent Long Short-Term Memory (LSTM) method based on discrete wavelet transform (DWT) and Complementary Ensemble Empirical Mode Decomposition (CEEMD) pre-processor methods in both temporal and spatial modes. It was investigated in five consecutive stations on the Savannah River. The results of analysis of models showed the ability and high efficiency of the method used in estimating the amount of dissolved oxygen in water. On the other hand, pre-processor methods improved the results. It was also observed in the investigations that the results of analysis based on wavelet transformation in spatial modeling reduced the RMSE error by two percent and also the empirical mode decomposition in temporal modeling by 15 percent. The best evaluation for test data was obtained using the empirical mode decomposition in temporal modeling corresponding to the previous day with values ​​of DC=0.977, R=0.988 and RMSE=0.017. Also, in the spatial modeling to estimate dissolved oxygen in the third station, it was found that the results obtained from the inputs of the dissolved oxygen parameter one day before the second station and two days before the first station have the best results.

Keywords

Main Subjects

References

  1. Adamowski, K., Prokoph, A., & Adamowski, J. (2009). Development of a new method of wavelet aided trend detection and estimation. Hydrological Processes, 23, 2686-2696.
  2. 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.
  3. Amirat, Y., Benbouzid, M. E. H., Wang, T., Bacha, K., & Feld, G. (2018). EEMD-based notch filter for induction machine bearing faults detection. Applied Acoustics, 133, 202-209.
  4. 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.
  5. Chou, C.-M. (2014). Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stochastic Environmental Research and Risk Assessment, 28(6), 1401-1408.
  6. 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.
  7. Dabrowski, J. J., Rahman, A., & George, A. (2018). Prediction of Dissolved Oxygen from pH and Water Temperature in Aquaculture Prawn Ponds Proceedings of the Australasian Joint Conference on Artificial Intelligence - Workshops, Wellington, New Zealand.
  8. 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.
  9. Eze, E. H. S. A. T. (2021). Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm Water, 13(13).
  10. Fang, X., & Yuan, Z. (2019). Performance enhancing techniques for deep learning models in time series forecasting. Engineering Applications of Artificial Intelligence, 85, 533-542.
  11. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-term Memory. Neural computation, 9, 1735-1780.
  12. Hosseinpanahi, B., Nikmehr, S., & Ebrahimi, K. (2021). Comparison of the support vector machine and radial function neural network models in predicting of SiminehRood river water quality Iran. Water and Irrigation Management, 11(3), 409-419.(In Persian)
  13. Ji, X., Shang, X., Dahlgren, R. A., & Zhang, M. (2017). Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China. Environmental Science and Pollution Research, 24(19), 16062-16076.
  14. Lau, K. M., & Weng, H. (1995). Climate Signal Detection Using Wavelet Transform: How to Make a Time Series Sing. Bulletin of the American Meteorological Society, 76(12), 2391-2402.
  15. Liu, S., Xu, L., Li, D., Li, Q., Jiang, Y., Tai, H., & Zeng, L. (2013). Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization. Computers and Electronics in Agriculture, 95, 82-91.
  16. 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.
  17. Nourani, V., Hosseini Baghanam, A., Adamowski, J., & Kisi, O. (2014). Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514, 358-377.
  18. 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.
  19. 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.
  20. Roushangar, K., & Ghasempour, R. (2020). Monthly precipitation prediction improving using the integrated model based on kernel-wavelet and complementary ensemble empirical mode decomposition. Amirkabir Journal of Civil Engineering, 52(10), 2649-2660.(In Persian)
  21. 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(p00814), 1467-1477.(In Persian)
  22. Sagheer, A., & Kotb, M. (2019). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203-213.
  23. 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.
  24. 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.
  25. 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.
  26. Wu, Z., & Huang, N. (2004). A study of the characteristics of white noise using the empirical mode decomposition method. Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences, 460, 1597-1611.
  27. Xu, C., Chen, X., & Zhang, L. (2021). Predicting river dissolved oxygen time series based on stand-alone models and hybrid wavelet-based models. Journal of Environmental Management, 295, 113085.
  28. Yadav, B., & Eliza, K. (2017). A hybrid wavelet-support vector machine model for prediction of Lake water level fluctuations using hydro-meteorological data. Measurement, 103, 294-301. 
Water and Irrigation Management
Volume 12, Issue 4
January 2023
Pages 983-890

Files

Share

How to cite

Statistics