Skip to main content
SpringerLink
Log in

Modeling of Dissolved Oxygen Using Genetic Programming Approach

  • Conference paper
  • First Online:
Theoretical Computer Science and Discrete Mathematics (ICTCSDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10398))

Abstract

Genetic Programming (GP) based modeling is suggested for modeling the variation of Dissolved Oxygen (DO) under controlled conditions in the presence and absence of toxicant. The results indicated that GP is able to evolve robust physically meaningful models even with small dataset by selecting the most relevant functions from the set of functions given for the modeling. It is interesting to note that the evolved models clearly reflect the underlying non-linearity of the process distinctly for both the case studies.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Archana, S., Prashant, P.: River water quality modelling using artificial neural network technique. Aquat. Procedia 4, 1070–1077 (2015)

    Article  Google Scholar 

  2. Antanasijevic, D., Pocajt, V., Povrenović, D., Perić-Grujić, A., Ristić, M.: Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environ. Sci. Pollut. Res. 20, 9006–9013 (2013)

    Article  Google Scholar 

  3. Antanasijevic, D., Pocajt, V., Peric-Grujic, A., Ristic, M.: Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis. J. Hydrol. 519, 1895–1907 (2014)

    Article  Google Scholar 

  4. Anyadike, C.C., Mbajiorgu, C.C., Ajah, G.N.: Prediction of the physico-chemical interactions of vimtim stream water quality using the aquatox model. IOSR J. Eng. 3(10), 1–6 (2013)

    Google Scholar 

  5. Areerachakul, S., Junsawang, P., Pomsathit, A.: Prediction of dissolved oxygen using artificial neural network. In: International Conference on Computer Communication and Management Proceedings of CSIT, vol. 5, Singapore, pp. 524–528 (2011)

    Google Scholar 

  6. Ay, M., Kisi, O.: Modeling of dissolved oxygen concentration using different neural network techniques in foundation Creek, El Paso County, Colorado. J. Environ. Eng. 138(6), 654–662 (2012)

    Article  Google Scholar 

  7. Chen, W.B., Liu, W.C.: Artificial neural network modeling of dissolved oxygen in reservoir. Environ. Monit. Assess. 186(2), 1203–1217 (2014)

    Article  Google Scholar 

  8. Deepshikha, S., Arun, K.: Assessment of river quality models: a review. Rev. Environ. Sci. Biotechnol. 12(3), 285–311 (2012)

    Google Scholar 

  9. Koelmans, A.A., Vander Heude, A., Knijff, L.M., Aalderink, R.H.: Integrated modelling of Eutrophication and organic contaminant fate and effects in aquatic ecosystems. a review. J. Water Resour. 35(15), 3517–3536 (2001)

    Google Scholar 

  10. Koza, J.R.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  11. Muttil, N., Chau, K.W.: Neural network and genetic programming for modelling coastal algal blooms. Int. J. Environ. Pollut. 28(3–4), 223–238 (2006)

    Article  Google Scholar 

  12. Park, R.A., Clough, J.S., Wellman, M.C.: Aquatox modeling environmental fate and ecological effects in aquatic ecosystems. J. Environ. Model. 213, 1–15 (2008)

    Google Scholar 

  13. Radwan, M., Willems, P., El-sadek, A., Berlamont, J.: Modelling of dissolved oxygen and biochemical oxygen demand in river water using a detailed and a simplified model. Intl. J. River Basin Manage. 1(2), 97–103 (2003)

    Article  Google Scholar 

  14. Schmid, B.H., Koskiaho, J.: Artificial neural network modeling of dissolved oxygen in a wetland pond: the case of Hovi, Finland. J. Hydrol. Eng. 11(2), 188–192 (2006)

    Article  Google Scholar 

  15. Sivapragasam, C., Muttil, N., Jeselia, M.C., Visweshwaran, S.: Infilling of rainfall information using genetic programming. Aquat. Procedia 4, 1016–1022 (2015)

    Article  Google Scholar 

  16. Sivapragasam, C., Mutti, N., Muthukumar, S., Arun, V.M.: Prediction of algal blooms using genetic programming. Mar. Pollut. Bull. 60, 1849–1855 (2010)

    Article  Google Scholar 

  17. Wen, X., Fang, J., Diao, M., Zhang, C.: Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China. Environ. Monit. Assess. 185(5), 4361–4371 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Center for Water Technology, Kalasalingam University, Krishnankovil, 626126, Tamil Nadu, India

    S. Vanitha, C. Sivapragasam & N. V. N. Nampoothiri

Authors
  1. S. Vanitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Sivapragasam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. V. N. Nampoothiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Vanitha .

Editor information

Editors and Affiliations

  1. Kalasalingam University, Krishnankoil, India

    S. Arumugam

  2. Ball State University, Muncie, Indiana, USA

    Jay Bagga

  3. Indiana University – Purdue University, Fort Wayne, Indiana, USA

    Lowell W. Beineke

  4. Indian Institute of Technology, New Delhi, India

    B.S. Panda

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Vanitha, S., Sivapragasam, C., Nampoothiri, N.V.N. (2017). Modeling of Dissolved Oxygen Using Genetic Programming Approach. In: Arumugam, S., Bagga, J., Beineke, L., Panda, B. (eds) Theoretical Computer Science and Discrete Mathematics. ICTCSDM 2016. Lecture Notes in Computer Science(), vol 10398. Springer, Cham. https://doi.org/10.1007/978-3-319-64419-6_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64419-6_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64418-9

  • Online ISBN: 978-3-319-64419-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation