An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction
Wastewater treatment is a pivotal step in water resource recycling. Predicting effluent wastewater quality can help wastewater treatment plants (WWTPs) establish efficient operations so as to save resources. We propose CNN-LSTM-Attention (CLATT), an attention-based effluent wastewater quality predic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/12/7011 |
| _version_ | 1797596316592766976 |
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| author | Yue Li Bin Kong Weiwei Yu Xingliang Zhu |
| author_facet | Yue Li Bin Kong Weiwei Yu Xingliang Zhu |
| author_sort | Yue Li |
| collection | DOAJ |
| description | Wastewater treatment is a pivotal step in water resource recycling. Predicting effluent wastewater quality can help wastewater treatment plants (WWTPs) establish efficient operations so as to save resources. We propose CNN-LSTM-Attention (CLATT), an attention-based effluent wastewater quality prediction model, which uses a convolutional neural network (CNN) as an encoder and a long short-term memory network (LSTM) as a decoder. An attention mechanism is used to aggregate the information at adjacent sampling times. A sliding window method is proposed to solve the problem of the prediction performance of the model decreasing with time. We conducted the experiment using data collected from a WWTP in Fujian, China. Our results show that the accuracy of prediction is improved, with MSE decreasing by 0.25, MAPE decreasing by 5% and LER decreasing by 7%, after using the sliding window method. Compared with other methods, CLATT achieves the fastest prediction speed among all the methods based on LSTM and the most accurate prediction performance, with MSE increasing up to 0.92, MAPE up to 0.08 and LER up to 0.11. Furthermore, we performed an ablation study on the proposed method to validate the rationality of the major part of the model, and the results show that the LSTM significantly improves the predictive performance of the model, and the CNN and the attention mechanism also improve the accuracy of the prediction. |
| first_indexed | 2024-03-11T02:48:50Z |
| format | Article |
| id | doaj.art-87dcac81ba8845ae9d519fe79f427114 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T02:48:50Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-87dcac81ba8845ae9d519fe79f4271142023-11-18T09:07:43ZengMDPI AGApplied Sciences2076-34172023-06-011312701110.3390/app13127011An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality PredictionYue Li0Bin Kong1Weiwei Yu2Xingliang Zhu3Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaHefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, ChinaWastewater treatment is a pivotal step in water resource recycling. Predicting effluent wastewater quality can help wastewater treatment plants (WWTPs) establish efficient operations so as to save resources. We propose CNN-LSTM-Attention (CLATT), an attention-based effluent wastewater quality prediction model, which uses a convolutional neural network (CNN) as an encoder and a long short-term memory network (LSTM) as a decoder. An attention mechanism is used to aggregate the information at adjacent sampling times. A sliding window method is proposed to solve the problem of the prediction performance of the model decreasing with time. We conducted the experiment using data collected from a WWTP in Fujian, China. Our results show that the accuracy of prediction is improved, with MSE decreasing by 0.25, MAPE decreasing by 5% and LER decreasing by 7%, after using the sliding window method. Compared with other methods, CLATT achieves the fastest prediction speed among all the methods based on LSTM and the most accurate prediction performance, with MSE increasing up to 0.92, MAPE up to 0.08 and LER up to 0.11. Furthermore, we performed an ablation study on the proposed method to validate the rationality of the major part of the model, and the results show that the LSTM significantly improves the predictive performance of the model, and the CNN and the attention mechanism also improve the accuracy of the prediction.https://www.mdpi.com/2076-3417/13/12/7011effluent wastewater quality predictionneural networksliding windowattention mechanism |
| spellingShingle | Yue Li Bin Kong Weiwei Yu Xingliang Zhu An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction Applied Sciences effluent wastewater quality prediction neural network sliding window attention mechanism |
| title | An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction |
| title_full | An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction |
| title_fullStr | An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction |
| title_full_unstemmed | An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction |
| title_short | An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction |
| title_sort | attention based cnn lstm method for effluent wastewater quality prediction |
| topic | effluent wastewater quality prediction neural network sliding window attention mechanism |
| url | https://www.mdpi.com/2076-3417/13/12/7011 |
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