Advertisement

Prediction of Dissolved Oxygen Concentration in Sewage Using Support Vector Regression Based on Fuzzy C-means Clustering

  • Xing-Liang Shi
  • Jian Zhou
  • Xiao-Feng Wang
  • Le Zou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

Abstract

In order to solve the problem of real-time measurement of dissolved oxygen in wastewater treatment process, a support vector regression algorithm based on fuzzy C-means clustering is proposed to predict the content of dissolved oxygen (DO) in sewage. Firstly, the whole samples are divided into many sub-samples by fuzzy C-mean clustering. Then, a support vector regression model is established on each sub sample. Compared with other prediction methods, the proposed model has good comprehensive prediction performance. It can satisfy the actual demand prediction of DO dissolved oxygen in sewage.

Keywords

Fuzzy clustering Support vector regression Dissolved oxygen DO prediction 

Notes

Acknowledgements

This work was supported by the grant of the National Natural Science Foundation of China, No.61672204, the grant of Major Science and Technology Project of Anhui Province, No.17030901026, the grant of Key Constructive Discipline Project of Hefei University, No. 2016xk05, the grant of the key Scientific Research Foundation of Education Department of Anhui Province, No. KJ2018A0555, KJ2017A542.

References

  1. 1.
    Liu, T.: Modeling and parameter optimization analysis of activated sludge system (A2/O). Harbin Institute of Technology (2017)Google Scholar
  2. 2.
    Henzm, M.: Activated sludge models ASM1, ASM2, ASM2d and ASM3, pp. 13–15. IW-A Publishing, London (2000)Google Scholar
  3. 3.
    Song, X.M.: Study on soft sensor model of wastewater measuring based on SVM. Nanchang University (2007)Google Scholar
  4. 4.
    Cheng, C.: Mixed multi-modeling soft measurement research on biological aerated filter sewage treatment. Anhui University of Technology (2016)Google Scholar
  5. 5.
    Huang, W.: Research on the engineering application of high concentration activated sludge process treating wastewater. Harbin Institute of Technology (2012)Google Scholar
  6. 6.
    Balafar, M.A.: Fuzzy C-mean based brain MRI segmentation algorithms. Artif. Intell. Rev. 413, 441–449 (2014)CrossRefGoogle Scholar
  7. 7.
    You, J.C., Mao, H.H.: Classification of seabed sediment using the fuzzy C mean clustering and support vector machine methods. Mar. Sci. 11, 122–130 (2014)Google Scholar
  8. 8.
    Ding, S.F., Qi, B.J., Tan, H.Y.: An overview on theory and algorithm of support vector machines. J. Univ. Electron. Sci. Technol. China 40(1), 2–7 (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xing-Liang Shi
    • 1
  • Jian Zhou
    • 1
  • Xiao-Feng Wang
    • 2
  • Le Zou
    • 2
  1. 1.Department of Environmental EngineeringHefei UniversityHefeiChina
  2. 2.Key Lab of Network and Intelligent Information Processing, Department of Computer Science and TechnologyHefei UniversityHefeiChina

Personalised recommendations