Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Water |
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| Online Access: | http://www.mdpi.com/2073-4441/10/7/873 |
| _version_ | 1798045328331505664 |
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| author | Tingting Yu Shuai Yang Yun Bai Xu Gao Chuan Li |
| author_facet | Tingting Yu Shuai Yang Yun Bai Xu Gao Chuan Li |
| author_sort | Tingting Yu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T23:18:53Z |
| format | Article |
| id | doaj.art-63ed2e4f7dab45a48646877689e98efa |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-11T23:18:53Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-63ed2e4f7dab45a48646877689e98efa2022-12-22T03:57:30ZengMDPI AGWater2073-44412018-06-0110787310.3390/w10070873w10070873Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning MachineTingting Yu0Shuai Yang1Yun Bai2Xu Gao3Chuan Li4College of Environment and Resources, Chongqing Technology and Business University, Chongqing 400067, ChinaNational Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, ChinaNational Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, ChinaNational Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, ChinaCollege of Environment and Resources, Chongqing Technology and Business University, Chongqing 400067, ChinaThe 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.http://www.mdpi.com/2073-4441/10/7/873kernel principal component analysisextreme learning machinewastewaterquality forecasting |
| spellingShingle | Tingting Yu Shuai Yang Yun Bai Xu Gao Chuan Li Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine Water kernel principal component analysis extreme learning machine wastewater quality forecasting |
| title | Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine |
| title_full | Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine |
| title_fullStr | Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine |
| title_full_unstemmed | Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine |
| title_short | Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine |
| title_sort | inlet water quality forecasting of wastewater treatment based on kernel principal component analysis and an extreme learning machine |
| topic | kernel principal component analysis extreme learning machine wastewater quality forecasting |
| url | http://www.mdpi.com/2073-4441/10/7/873 |
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