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.
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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.
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Shi, XL., Zhou, J., Wang, XF., Zou, L. (2018). Prediction of Dissolved Oxygen Concentration in Sewage Using Support Vector Regression Based on Fuzzy C-means Clustering. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_18
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DOI: https://doi.org/10.1007/978-3-319-95933-7_18
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