Environmental Science and Pollution Research

, Volume 26, Issue 19, pp 19879–19896 | Cite as

Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China

  • Lei Li
  • Peng JiangEmail author
  • Huan Xu
  • Guang Lin
  • Dong Guo
  • Hui Wu
Research Article


Water quality prediction is an effective method for managing and protecting water resources by providing an early warning against water quality deterioration. In general, the existing water quality prediction methods are based on a single shallow model which fails to capture the long-term dependence in historical time series and is more likely to cause a high rate of false alarms and false negatives in practical water monitoring application. To resolve these problems, a new model combining recurrent neural network (RNN) with improved Dempster/Shafer (D-S) evidence theory (RNNs-DS) is proposed in this paper. Among them, the RNNs which can handle the long-term dependence in historical time series effectively are used to realize the preliminary prediction of water quality. And the improved D-S evidence theory is used to synthesize the prediction results of RNNs. In addition, an improved strategy based on correlation analysis method is presented for evidence theory to obtain the number of evidence, which reduces uncertainty in evidence selection effectively. Besides, a new basic probability assignment function which based on modified softmax function is proposed. The new function can effectively solve the problems of weight allocation failure in the traditional function. Then, data about permanganate index, pH, total phosphorus, and dissolved oxygen from Jiuxishuichang monitoring station near Qiantang River, Zhejiang Province, China is used to verify the proposed model. Compared with support vector regression (SVR) and backpropagation neural network (BPNN) and three RNN models, the new model shows higher accuracy and better stability as indicated by four indices. Finally, the engineering application of the RNNs-DS algorithm has been realized on the self-developed water environmental monitoring and forecasting system, which can provide effective support for early risk assessment and prevention in water environment.


Recurrent neural network Evidence theory Water quality prediction Temporal correlation analysis Multiscale predictions Support vector regression Backpropagation neural network 


Funding information

This study is supported by International Science and Technology Cooperation Program of Zhejiang Province for Joint Research in High-tech Industry (No.2016C54007), National Key R&D Program of China (No.2016YFC0201400), Leading Talents of Science and Technology Innovation in Zhejiang Provincial Ten Thousands Plan (No. 2019R52040), Provincial Key R&D Program of Zhejiang Province (No.2017C03019) and National Natural Science Foundation of China and Zhejiang Joint Fund for Integrating of Informatization and Industrialization (No. U1509217).


  1. Abudu S, King JP, Bawazir AS (2010) Forecasting monthly streamflow of spring-summer runoff season in Rio Grande headwaters basin using stochastic hybrid modeling approach. J Hydrol Eng 16(4):384–390Google Scholar
  2. Alizadeh MJ, Nodoushan EJ, Kalarestaghi N et al (2017) Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models. Environ Sci Pollut Res 24(36):28017–28025Google Scholar
  3. 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. Environ Sci Pollut Res 20(12):9006–9013Google Scholar
  4. Arya FK, Zhang L (2015) Time series analysis of water quality parameters at Stillaguamish River using order series method. Stoch Env Res Risk A 29(1):227–239Google Scholar
  5. Barzegar R, Adamowski J, Moghaddam AA (2016) Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran. Stoch Env Res Risk A 30(7):1797–1819Google Scholar
  6. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166Google Scholar
  7. Box GEP, Jenkins GM (2010) Time series analysis: forecasting and control. J Time 31(4):303–303Google Scholar
  8. Campisi-Pinto S, Adamowski J, Oron G (2012) Forecasting urban water demand via wavelet-denoising and neural network models. Case study: city of Syracuse, Italy. Water Resour Manag 26(12):3539–3558Google Scholar
  9. Chau KW (2005) Characterization of transboundary POP contamination in aquatic ecosystems of Pearl River delta. Mar Pollut Bull 51(8–12):960–965Google Scholar
  10. Chen XY, Chau KW (2016) A hybrid double feedforward neural network for suspended sediment load estimation. Water Resour Manag 30(7):2179–2194Google Scholar
  11. Cho K, Van Merriënboer B, Bahdanau D, et al (2014) On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint, arXiv:1409.1259 Google Scholar
  12. Chubarenko I, Tchepikova I (2001) Modelling of man-made contribution to salinity increase into the Vistula lagoon (Baltic Sea). Ecol Model 138(1–3):87–100Google Scholar
  13. Chung J, Gulcehre C, Cho K, et al (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint, arXiv:1412.3555 Google Scholar
  14. Deng W, Wang G, Zhang X et al (2014) Water quality prediction based on a novel hybrid model of ARIMA and RBF neural network. IEEE International Conference on Cloud Computing & Intelligence Systems, pp 33–40Google Scholar
  15. Duan W, He B, Takara K, Luo P, Nover D, Sahu N, Yamashiki Y (2013a) Spatiotemporal evaluation of water quality incidents in Japan between 1996 and 2007. Chemosphere 93(6):946–953Google Scholar
  16. Duan W, Takara K, He B et al (2013b) Spatial and temporal trends in estimates of nutrient and suspended sediment loads in the Ishikari River, Japan, 1985 to 2010. Sci Total Environ 461:499–508Google Scholar
  17. Duan W, He B, Nover D, Yang G, Chen W, Meng H, Zou S, Liu C (2016) Water quality assessment and pollution source identification of the eastern Poyang Lake Basin using multivariate statistical methods. Sustainability 8(2):133Google Scholar
  18. Duan W, He B, Chen Y, Zou S, Wang Y, Nover D, Chen W, Yang G (2018) Identification of long-term trends and seasonality in high-frequency water quality data from the Yangtze River basin, China. PLoS One 13(2):e0188889Google Scholar
  19. Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211Google Scholar
  20. Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E (2014) Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol 11(3):645–656Google Scholar
  21. Faruk DÖ (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell 23(4):586–594Google Scholar
  22. Ghavidel SZZ, Montaseri M (2014) Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin. Stoch Env Res Risk A 28(8):2101–2118Google Scholar
  23. Goodfellow I, Bengio Y, Courville A (2016) Deep learning, vol 1. MIT Press, Cambridge, pp 367–415Google Scholar
  24. Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232Google Scholar
  25. Heddam S, Kisi O (2017) Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environ Sci Pollut Res 24(20):1–23Google Scholar
  26. Hou D, He H, Huang P, Zhang G, Loaiciga H (2013) Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method. Meas Sci Technol 24(5):055801Google Scholar
  27. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425Google Scholar
  28. Huang F, Wang X, Lou L et al (2010) Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques. Water Res 44(5):0–1572Google Scholar
  29. Huang P, Jin Y, Hou D et al (2017) Online classification of contaminants based on multi-classification support vector machine using conventional water quality sensors. Sensors 17(3):581Google Scholar
  30. Hui KH, Lim MH, Leong MS, al-Obaidi SM (2017) Dempster-Shafer evidence theory for multi-bearing faults diagnosis. Eng Appl Artif Intell 57:160–170Google Scholar
  31. Jiang P, Hu Z, Liu J, Yu S, Wu F (2016) Fault diagnosis based on chemical sensor data with an active deep neural network. Sensors 16(10):1695Google Scholar
  32. Jozefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of recurrent network architectures. In: International Conference on Machine Learning, pp 2342–2350Google Scholar
  33. Kim SE, Seo IW (2015) Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers. J Hydro Environ Res 9(3):325–339Google Scholar
  34. Kinerson RS, Kittle JL, Duda PB (2009) BASINS: better assessment science integrating point and nonpoint sources. In: Decision Support Systems for Risk-Based Management of Contaminated Sites. Springer, Berlin, pp 1–24Google Scholar
  35. Kumar DN, Raju KS, Sathish T (2004) River flow forecasting using recurrent neural networks. Water Resour Manag 18(2):143–161Google Scholar
  36. Le Hegarat-Mascle S, Bloch I, Vidal-Madjar D (1997) Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens 35(4):1018–1031Google Scholar
  37. Lee S, Lee D (2018) Improved prediction of harmful algal blooms in four Major South Korea’s Rivers using deep learning models. Int J Environ Res Public Health 15(7):1322Google Scholar
  38. Li S, Liu G, Tang X, Lu J, Hu J (2017a) An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis. Sensors 17(8):1729Google Scholar
  39. Li X, Peng L, Yao X, Cui S, Hu Y, You C, Chi T (2017b) Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollut 231:997–1004Google Scholar
  40. Li Z, Peng F, Niu B, Li G, Wu J, Miao Z (2018) Water quality prediction model combining sparse auto-encoder and LSTM network. IFAC-PapersOnLine 51(17):831–836Google Scholar
  41. Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y (2013) A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model 58(3–4):458–465Google Scholar
  42. Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. Journal of Big Data 2(1):1Google Scholar
  43. Najah A, El-Shafie A, Karim OA et al (2013) Application of artificial neural networks for water quality prediction. Neural Comput & Applic 22(1):187–201Google Scholar
  44. Olyaie E, Banejad H, Chau KW, Melesse AM (2015) A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ Monit Assess 187(4):189Google Scholar
  45. Park SS, Lee YS (1996) A multiconstituent moving segment model for water quality predictions in steep and shallow streams. Ecol Model 89(1–3):121–131Google Scholar
  46. Shafer G (1976) A mathematical theory of evidence. Princeton university press, PrincetonGoogle Scholar
  47. Shamshirband S, Jafari Nodoushan E, Adolf JE, Abdul Manaf A, Mosavi A, Chau KW (2019) Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Engineering Applications of Computational Fluid Mechanics 13(1):91–101Google Scholar
  48. Shang C, Yang F, Huang D, Lyu W (2014) Data-driven soft sensor development based on deep learning technique. J Process Control 24(3):223–233Google Scholar
  49. Si L, Wang Z, Tan C, Liu X (2014) A novel approach for coal seam terrain prediction through information fusion of improved D–S evidence theory and neural network. Measurement 54:140–151Google Scholar
  50. Tao Y, Ren BT (2012) Improvement of evidence compound rule based on partial conflict allocation strategies. Comput Eng 38(15):268–270Google Scholar
  51. Tiwari MK, Adamowski J (2013) Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour Res 49(10):6486–6507Google Scholar
  52. Wang WC, Xu DM, Chau KW, Lei GJ (2014) Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resour Manag 28(12):4183–4200Google Scholar
  53. Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78(10):1550–1560Google Scholar
  54. Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1(2):270–280Google Scholar
  55. Xiang SL, Liu ZM, Ma L (2006) Study of multivariate linear regression analysis model for ground water quality prediction. Guizhou Science 24(1):60–62Google Scholar
  56. Yi-Bo L (2010) Based on DS evidence theory of information fusion improved method. In: 2010 International Conference on Computer Application and System Modeling (ICCASM), vol 1, pp V1–V416Google Scholar
  57. Zeng M, Feng Y, Liu D et al (2008) Electricity price forecasting based on multi-models combined by evidential theory. Proceedings of the CSEE 16:016Google Scholar
  58. Zhang N, Lai S (2011) Water quantity prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks. In: International joint conference on neural networks, California, International Joint Conference on Neural Networks, pp 2172–2176Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lei Li
    • 1
  • Peng Jiang
    • 1
    Email author
  • Huan Xu
    • 1
  • Guang Lin
    • 2
  • Dong Guo
    • 3
  • Hui Wu
    • 4
  1. 1.College of AutomationHangzhou Dianzi UniversityHangzhouChina
  2. 2.Zhejiang Provincial Environmental Monitoring CenterHangzhouChina
  3. 3.College of Electrical EngineeringZhejiang University of Water Resources and Electric PowerHangzhouChina
  4. 4.Fuzhou Fuguang Water Technology Co., LtdFuzhouChina

Personalised recommendations