SpringerLink
Log in

Evaluation of dissolved oxygen in water by artificial neural network and sample optimization

  • Published:
Journal of Central South University of Technology Aims and scope Submit manuscript

Abstract

Three important factors influencing directly the dissolved oxygen (DO) of river including the outflow, the water temperature and the pH, were used as input parameters to set up a BP neural network based on Levenberg-Marquant algorithm. The neural network model was proposed to evaluate DO in water. The model contains two parts: firstly, the learning sample is unified; secondly, the neural network is used to train the unified samples to ensure the best node number of hidden layer. The proposed model is applied to assessing the DO concentration of the Yellow River in Lanzhou city. The evaluation result is compared with that by the neural network method and the reported result in Lanzhou city. The comparison result indicates that the performance of the neural network model is practically feasible in the assessment of DO. At the same time, the linear interpolation method can add the number of network’s learning sample to improve the prediction precision of the network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. MA Rui-jie, LI **n, LIU **ao-duan, XU Qing. An application study of connection model between reservoir organic pollutant BOD-DO and temperature [J]. Rock and Mineral Analysis, 2005, 24(2): 105–108. (in Chinese)

    Google Scholar 

  2. GUO **-song, HUO Guo-you, LONG Teng-rui. Study on BOD-DO coupling for water quality simulation with artificial neural network [J]. Acta Scientiae Circumstantiae, 2001, 21(2): 140–143.

    Google Scholar 

  3. GUO Lian-xi, DENG Chang-hui. Prediction model for dissolved oxygen in fish pond based on fuzzy neural network [J]. Journal of Fisheries of China, 2006, 30(2): 225–229. (in Chinese)

    MathSciNet  Google Scholar 

  4. YANG **ao-ming, LI Ming-huan, YANG Pu, JIA Ming-xing. Dissolved oxygen prediction model and its error revision [J]. Control Engineering of China, 2004, 11(2): 127–129, 137.

    Google Scholar 

  5. FU Guo-wei, et al. Water pollution control planning [M]. Bei**g: Tsinghua University Press, 1985: 185–187. (in Chinese)

    Google Scholar 

  6. YANG Mei-ni, LI Ding-fang. FANN-based surface water quality evaluation model and its application in the Shaoguan area [J]. Geo-spatial Information Science, 2007, 10(4): 303–310.

    Article  Google Scholar 

  7. WAN You-chuan, XIE Hong-yu, WU Zheng-bing, SHEN **ao-li. Application of artificial neural network and GIS to water quality evaluation [J]. Journal of Wuhan University: Engineering, 2003, 36(3): 7–12.

    Google Scholar 

  8. GUO Zhong-yang, CHEN Zhong-yuan, LI Lu-qian, SONG Bao-**, LU Yan. Artificial neural network and its application in regime prediction of ground water quality [J]. Journal of East China Normal University: Science, 2001(1): 84–89. (in Chinese)

    Google Scholar 

  9. CHEN Shou-yu, LI Ya-wei. Water quality evaluation based on fuzzy artificial neural network [J]. Advances in Water Science, 2005, 16(1): 88–91.

    Google Scholar 

  10. SHU **. Using neural network model to predict water quality [J]. Environmental Science and Management, 2006, 31(1): 44–46. (in Chinese)

    Google Scholar 

  11. KUO Yi-ming, LIU Chen-ying, LIN Kao-hong. Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of Blackfoot disease in Taiwan [J]. Water Research, 2004, 36(1): 148–158.

    Article  Google Scholar 

  12. LIU Lian-fang, WEI Li-hong, LI Ai-ming. Neural network apply in water evaluation of the Liao River [J]. Urban Environment and Urban Ecology, 2003, 16(6): 251–253. (in Chinese)

    Google Scholar 

  13. CHEN Li-hua, CHANG Qin-chun, CHEN **ng-guo. Application of BP networks to predict water quality of the Yellow River [J]. Journal of Lanzhou University: Natural Science, 2003, 39(2): 53–56. (in Chinese)

    MathSciNet  Google Scholar 

  14. SOVAN L, GUEGAN J F. Artificial neural networks as a tool in ecological modeling, an introduction [J]. Ecological Modeling, 1999, 120(2): 65–73.

    Article  Google Scholar 

  15. ABRAHART R J, LINDA S. Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments [J]. Hydrological Processes, 2000, 14(1): 2157–2172.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Experiment Center, Northwest University for Nationality, Lanzhou, 730030, China

    Li-hua Chen  (陈丽华)

  2. College of Chemical Engineering, Northwest University for Nationality, Lanzhou, 730030, China

    Li Li  (** 丽)

Authors
  1. Li-hua Chen  (陈丽华)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Li  (** 丽)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li-hua Chen  (陈丽华).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, Lh., Li, L. Evaluation of dissolved oxygen in water by artificial neural network and sample optimization. J. Cent. South Univ. Technol. 15 (Suppl 2), 416–420 (2008). https://doi.org/10.1007/s11771-008-0498-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-008-0498-5

Key words

Search

Navigation