**5. Conclusions**

To improve the accuracy of water quality prediction data, this study proposed the ANN-WT-LSTM model based on an artificial neural network, wavelet transform, and long short-term memory network, using the water quality data of the Jinjiang River basin in China as the research object for prediction analysis. For missing water quality data caused by instrument failure, this study used an artificial neural network to fill in the missing values based on the time-series information of water quality data. Then, we used wavelet transform to decompose and reconstruct the water quality time series, in order to remove the impact of short-term random disturbance noise, improve the prediction accuracy of the model on out-of-sample data, and the ability to predict future dynamic trends, so that it can more effectively predict the short-term as well as long-term dynamic trends in water quality time-series data. Subsequently, compared with the ANN-LSTM model and the NAR neural network model, the results show that the ANN-WT-LSTM proposed in this study is better than other models in all evaluation indexes, and the model effectively improves the accuracy of water quality prediction, which is significant for water environment protection. The study not only provides vital data support for water quality safety management decisions, but also has important theoretical and practical significance for safeguarding the sustainable development of the riverine areas and water environmental protection in the reservoir area.

This study predicts the possible future situation of reservoir water quality through the study of time series. However, due to the limitation of monitoring conditions, it can only predict the water quality at one point of the reservoir, which cannot reflect the overall spatial change of water quality. Therefore, in order to establish a more perfect reservoir early warning system, we suggest that water quality monitoring points be set up in many places to monitor the water quality in different directions of the reservoir to combine water quality prediction with GIS technology. In this way, we not only study the development trend of water quality in time, but also study the change of water quality in space, so as to combine time and space prediction and lay a good foundation for establishing a perfect water quality early warning system.

**Author Contributions:** Conceptualization, J.W.; methodology, J.W. and Z.W.; supervision, J.W.; investigation, J.W.; visualization, J.W.; writing—original draft, J.W. and Z.W.; writing—review and editing, J.W. and Z.W.; validation, J.W. and Z.W.; diagram and flowchart preparation, Z.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Open Research Fund of the State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research (grant No. IWHR-SKL-201905).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding authors. The data are not publicly available due to the continuation of a follow-up study by the authors.

**Acknowledgments:** We would like to thank the computing science center of Shanghai Ocean University for its support in the scientific research.

**Conflicts of Interest:** The authors declare no conflict of interest.
