Predicting water quality in municipal water management systems using a hybrid deep learning model
Increasing municipal waste generation puts more and more municipal water resources at high risk. Accurate prediction of water quality becomes critical for effective protection of the water resources. Due to the nonlinear and non-stationary characteristics of water quality data of the municipal water...
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| Published in: | Engineering applications of artificial intelligence Vol. 133; p. 108420 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.07.2024
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| ISSN: | 0952-1976, 1873-6769 |
| Online Access: | Get full text |
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| Abstract | Increasing municipal waste generation puts more and more municipal water resources at high risk. Accurate prediction of water quality becomes critical for effective protection of the water resources. Due to the nonlinear and non-stationary characteristics of water quality data of the municipal water resources, it is challenging to achieve high prediction accuracy, especially for medium-term and long-term predictions. To address this issue, we propose a novel hybrid deep learning model to predict water quality multiple steps ahead. The proposed model adopts the encoder–decoder structure in the form of two long short-term memory (LSTM) networks, integrated with the attention mechanism and a convolutional neural network (CNN). The model extracts the complex correlation between multiple water quality features through the CNN, and uses the two LSTM networks to transfer historical information to predictions, with an attention layer assigning different weights to the different parts of the historical information. Using three years of water quality data collected from an urban river, we experimentally show that the proposed model outperforms the baseline models by 11%–34% in root mean squared error (RMSE) when predicting dissolved oxygen multiple steps ahead, and by 1%–7% when predicting total phosphorus. Similar improvement has also been found in Nash–Sutcliffeefficiency (NSE) and mean absolute error (MAE). The proposed model is a feasible solution for multi-step medium-term water quality prediction. |
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| AbstractList | Increasing municipal waste generation puts more and more municipal water resources at high risk. Accurate prediction of water quality becomes critical for effective protection of the water resources. Due to the nonlinear and non-stationary characteristics of water quality data of the municipal water resources, it is challenging to achieve high prediction accuracy, especially for medium-term and long-term predictions. To address this issue, we propose a novel hybrid deep learning model to predict water quality multiple steps ahead. The proposed model adopts the encoder–decoder structure in the form of two long short-term memory (LSTM) networks, integrated with the attention mechanism and a convolutional neural network (CNN). The model extracts the complex correlation between multiple water quality features through the CNN, and uses the two LSTM networks to transfer historical information to predictions, with an attention layer assigning different weights to the different parts of the historical information. Using three years of water quality data collected from an urban river, we experimentally show that the proposed model outperforms the baseline models by 11%–34% in root mean squared error (RMSE) when predicting dissolved oxygen multiple steps ahead, and by 1%–7% when predicting total phosphorus. Similar improvement has also been found in Nash–Sutcliffeefficiency (NSE) and mean absolute error (MAE). The proposed model is a feasible solution for multi-step medium-term water quality prediction. |
| ArticleNumber | 108420 |
| Author | Shu, Jiabin Huang, Leijun Wang, Wei Luo, Wenxian Guo, Wenjie Feng, Hailin Xia, Kai Fang, Kai |
| Author_xml | – sequence: 1 givenname: Wenxian surname: Luo fullname: Luo, Wenxian organization: Zhejiang A & F University, 666 Wusu Street, Hangzhou, Zhejiang, 311300, China – sequence: 2 givenname: Leijun orcidid: 0000-0002-4149-7098 surname: Huang fullname: Huang, Leijun organization: Zhejiang A & F University, 666 Wusu Street, Hangzhou, Zhejiang, 311300, China – sequence: 3 givenname: Jiabin surname: Shu fullname: Shu, Jiabin organization: Quzhou Digital Rural Construction Center, 139 Fushi Road, Quzhou, Zhejiang, 324000, China – sequence: 4 givenname: Hailin surname: Feng fullname: Feng, Hailin email: hlfeng@zafu.edu.cn organization: Zhejiang A & F University, 666 Wusu Street, Hangzhou, Zhejiang, 311300, China – sequence: 5 givenname: Wenjie surname: Guo fullname: Guo, Wenjie organization: Zhejiang A & F University, 666 Wusu Street, Hangzhou, Zhejiang, 311300, China – sequence: 6 givenname: Kai surname: Xia fullname: Xia, Kai organization: Zhejiang A & F University, 666 Wusu Street, Hangzhou, Zhejiang, 311300, China – sequence: 7 givenname: Kai orcidid: 0000-0003-0419-1468 surname: Fang fullname: Fang, Kai email: kaifang@zafu.edu.cn organization: Zhejiang A & F University, 666 Wusu Street, Hangzhou, Zhejiang, 311300, China – sequence: 8 givenname: Wei surname: Wang fullname: Wang, Wei organization: Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing, Artificial Intelligence Research Institute, Shenzhen MSU-BIT University, Shenzhen, Guangdong, 518172, China |
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| Keywords | Long short-term memory Attention mechanism Multi-step prediction Convolutional neural network Encoder–decoder structure Water quality |
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