Abstract
Aiming at the problem that the detection accuracy of effluent COD (chemical oxygen demand) in sewage treatment needs to be further improved, a combined model based on support vector machine and neural network is proposed to predict effluent COD. It can reduce the influence of local optimum on the global scope so as to improve the accuracy of prediction. Firstly, the sample data are divided into two categories by support vector machine. Then the BP neural network model and the Echo State Network (ESN) model are established on two sub-samples respectively. Compared with single neural network model, the mean absolute error and root mean square error of combined model are both reduced. Besides, the proposed model has better comprehensive prediction performance and can meet the actual demand of effluent COD prediction in sewage treatment.
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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 Anhui Provincial Natural Science Foundation, No. 1908085MF184, the grant of Teaching Team of Anhui Province, No. 2016jxtd101, the grant of Natural Science Foundation of Hefei University, No. 0391648022.
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Zhou, J., Huang, QJ., Wang, XF., Zou, L. (2019). Prediction of Chemical Oxygen Demand in Sewage Based on Support Vector Machine and Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_18
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DOI: https://doi.org/10.1007/978-3-030-26763-6_18
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