# An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction
## 5 Conclusions  
In this article, we present CLATT, which uses the CNN-LSTM-Attention structure as its backbone. Seven-month wastewater quality data collected from WWTP were used to validate the effectiveness of the proposed method. We conducted numerous comparative experiments and an ablation study. In order to eliminate the decreasing trend of prediction performance over time, a sliding window method is proposed to improve the prediction accuracy and stability of the model. In addition, an ablation study was conducted on the main structure of the model to verify its rationality. Finally, compared with other methods, the proposed method achieved optimal performance on different evaluation indicators, and realized the most efficient prediction among all the methods based on the LSTM. Future work will focus on the following:  
* Improving the generalization performance of the model. The proposed method in this paper is mainly used for the data of municipal WWTPs. However, in practical application, the wastewater quality condition of WWTPs varies with different water sources. For example, the COD of chemical wastewater is higher than that of municipal wastewater. Different water quality conditions will affect the prediction performance  
Figure 12: PCMs of (**a**) the target and predicted values of COD with four methods; (**b**) the target and predicted values of NH3-N with four methods; (**c**) the target and predicted values of TP with four methods; (**d**) the target and predicted values of TN with four methods.  
of the method. The method proposed in this paper may not be suitable for use in WWTPs from other water sources. Future work could consider building more general models to adapt to the data characteristics of different WWTPs and improve the generalization performance of the method.
* Adding the means of operation in WWTPs to the input of the model. The sampling information of WWTPs only includes the indicators of influent and effluent wastewater quality; it does not include the means of operation, such as the dosage of commonly used chemicals in WWTPs and the aeration capacity of the blower, which are also important factors affecting the quality of effluent wastewater. The proposed method in this paper is only based on the use of influent wastewater quality to predict effluent wastewater quality. In the future, the operation methods of WWTPs could be added to realize the prediction of effluent wastewater quality under the condition of complete data collection.  
Conceptualization, B.K.; methodology, Y.L. and W.Y.; software, Y.L.; validation, Y.L. and W.Y.; formal analysis, Y.L. and X.Z.; investigation, Y.L. and W.Y.; data curation, X.Z.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and B.K.; visualization, Y.L.; supervision, B.K.; project administration, B.K.; funding acquisition, B.K. All authors have read and agreed to the published version of the manuscript.  
This research is funded by AIWater (Anhui) Co., Ltd.  
Not applicable.  
The authors would like to thank [PERSON] from AIWater for proposing this research project and providing expertise and research data. His participation in the discussions and constructive professional suggestions helped with our research progress and paper preparation.  
The authors declare no conflict of interest.