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Volume 14
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10.3390/su142013231
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Article

Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia

by
Honglei Chen
1,2,
Junbo Yang
1,2,
Xiaohua Fu
1,2,*,
Qingxing Zheng
1,2,
Xinyu Song
1,2,
Zeding Fu
3,
Jiacheng Wang
1,2,
Yingqi Liang
1,2,
Hailong Yin
1,2,
Zhiming Liu
4,*,
Jie Jiang
1,2,
He Wang
1,2 and
Xinxin Yang
1,2
1
Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
2
School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China
3
School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China
4
Department of Biology, Eastern New Mexico University, Portales, NM 88130, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(20), 13231; https://doi.org/10.3390/su142013231
Submission received: 1 September 2022 / Revised: 29 September 2022 / Accepted: 9 October 2022 / Published: 14 October 2022
(This article belongs to the Special Issue Advance in Time Series Modelling for Water Resources Management)
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Abstract

Prediction of water quality is a critical aspect of water pollution control and prevention. The trend of water quality can be predicted using historical data collected from water quality monitoring and management of water environment. The present study aims to develop a long short-term memory (LSTM) network and its attention-based (AT-LSTM) model to achieve the prediction of water quality in the Burnett River of Australia. The models developed in this study introduced an attention mechanism after feature extraction of water quality data in the section of Burnett River considering the effect of the sequences on the prediction results at different moments to enhance the influence of key features on the prediction results. This study provides one-step-ahead forecasting and multistep forward forecasting of dissolved oxygen (DO) of the Burnett River utilizing LSTM and AT-LSTM models and the comparison of the results. The research outcomes demonstrated that the inclusion of the attention mechanism improves the prediction performance of the LSTM model. Therefore, the AT-LSTM-based water quality forecasting model, developed in this study, demonstrated its stronger capability than the LSTM model for informing the Water Quality Improvement Plan of Queensland, Australia, to accurately predict water quality in the Burnett River.
Keywords: water quality prediction; time series; attention mechanism; long short-term memory (LSTM) water quality prediction; time series; attention mechanism; long short-term memory (LSTM)

Share and Cite

MDPI and ACS Style

Chen, H.; Yang, J.; Fu, X.; Zheng, Q.; Song, X.; Fu, Z.; Wang, J.; Liang, Y.; Yin, H.; Liu, Z.; et al. Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia. Sustainability 2022, 14, 13231. https://doi.org/10.3390/su142013231

AMA Style

Chen H, Yang J, Fu X, Zheng Q, Song X, Fu Z, Wang J, Liang Y, Yin H, Liu Z, et al. Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia. Sustainability. 2022; 14(20):13231. https://doi.org/10.3390/su142013231

Chicago/Turabian Style

Chen, Honglei, Junbo Yang, Xiaohua Fu, Qingxing Zheng, Xinyu Song, Zeding Fu, Jiacheng Wang, Yingqi Liang, Hailong Yin, Zhiming Liu, and et al. 2022. "Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia" Sustainability 14, no. 20: 13231. https://doi.org/10.3390/su142013231

APA Style

Chen, H., Yang, J., Fu, X., Zheng, Q., Song, X., Fu, Z., Wang, J., Liang, Y., Yin, H., Liu, Z., Jiang, J., Wang, H., & Yang, X. (2022). Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia. Sustainability, 14(20), 13231. https://doi.org/10.3390/su142013231

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