Abstract
The water environment plays an essential role in the mangrove wetland ecosystem. Predicting water quality will help us better protect water resources from pollution, allowing the mangrove ecosystem to perform its normal ecological role. New approaches to solve such nonlinear problems need further research since the complexity of water quality data and they are easily affected by the noise. In this paper, we propose a water quality prediction model named CNN-LSTM with Attention (CLA) to predict the water quality variables. We conduct a case study on the water quality dataset of Beilun Estuary to predict pH and NH3-N. Linear interpolation and wavelet techniques are used for missing data filling and data denoising, respectively. The hybrid model CNN-LSTM is highly capable of resolving nonlinear time series prediction problems, and the attention mechanism captures longer time dependence. The experimental results show that our model outperforms other ones, and can predict with different time lags in a stable manner.
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Availability of data and materials
The data that support the findings of this study are available from the China National Environmental Monitoring Centre but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the China National Environmental Monitoring Centre.
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Acknowledgements
The authors thank the water quality data provided by the China National Environmental Monitoring Centre and the meteorological data provided by the National Oceanic and Atmospheric Administration. The authors thank ZiKai Liao for his valuable suggestions on the revision and proofreading of the paper.
Funding
This work was supported by the Major Science & Technology Program of Guangxi (Grant No. GKAA17129002), the Major Special Program of Chongqing Science & Technology Commission (No. CSTC 2019jscx-zdztzxX0031), Graduate Scientific Research and Innovation Foundation of Chongqing (No. CYB20067).
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: QZ collected the data. YY1 and CW implemented the methods mentioned in this paper. YY1 was a major contributor and wrote the manuscript under the guidance of QX. YY1 is mainly responsible for proofreading the manuscript. All authors read and approved the final manuscript.
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Yang, Y., Xiong, Q., Wu, C. et al. A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism. Environ Sci Pollut Res 28, 55129–55139 (2021). https://doi.org/10.1007/s11356-021-14687-8
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DOI: https://doi.org/10.1007/s11356-021-14687-8