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Water 2018, 10(7), 873; https://doi.org/10.3390/w10070873

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

1,2
,
2
,
2,* , 2,3
and
1,2
1
College of Environment and Resources, Chongqing Technology and Business University, Chongqing 400067, China
2
National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China
3
Chongqing Water Asset Management Co., Ltd., Chongqing 400015, China
*
Author to whom correspondence should be addressed.
Received: 22 May 2018 / Revised: 20 June 2018 / Accepted: 26 June 2018 / Published: 30 June 2018
(This article belongs to the Section Water Quality and Ecosystems)
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Abstract

The stable operation of sewage treatment is of great significance to controlling regional water environment pollution. It is also important to forecast the inlet water quality accurately, which may ensure the purification efficiency of sewage treatment at a low cost. In this paper, a combined kernel principal component analysis (KPCA) and extreme learning machine (ELM) model is established to forecast the inlet water quality of sewage treatment. Specifically, KPCA is employed for feature extraction and dimensionality reduction of the inlet wastewater quality and ELM is utilized for the future inlet water quality forecasting. The experimental results indicated that the KPCA-ELM model has a higher accuracy than the other comparison PCA-ELM model, ELM model, and back propagation neural network (BPNN) model for forecasting COD and BOD concentration of the inlet wastewater, with mean absolute error (MAE) values of 2.322 mg/L and 1.125 mg/L, mean absolute percentage error (MAPE) values of 1.223% and 1.321%, and root mean square error (RMSE) values of 3.108 and 1.340, respectively. It is recommended from this research that the method may provide a reliable and effective reference for forecasting the water quality of sewage treatment. View Full-Text
Keywords: kernel principal component analysis; extreme learning machine; wastewater; quality forecasting kernel principal component analysis; extreme learning machine; wastewater; quality forecasting
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Yu, T.; Yang, S.; Bai, Y.; Gao, X.; Li, C. Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine. Water 2018, 10, 873.

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