## 5 Conclusions  
In this case study, by assigning different weights to the variables affecting the comprehensive water quality index, WQI, according to their strength of influence, and conducting experiments with 10 different groupings respectively, it was demonstrated that in the process of long sequence time series forecasting, the corresponding optimal variable groupings are not the same for different Deep Learning models. Therefore, in the process of water quality prediction, adjusting the variables involved in the prediction is also an important part of the model performance, and through comparative experiments, this paper provides the best combination of variables relative to this one specific area of Chaohu Lake to provide guidance for subsequent research.  
After that, we shift the water quality forecasting from short sequence to long sequence time series forecasting. In the selection of models, we chose six different deep learning models including four classical models RNN, LSTM, MLP, and Transformer in the field of short-term forecasting, and two newly proposed long-term forecasting models Informer and FEDformer. The prediction effectiveness of each model is evaluated by two different performance evaluation parameters, MSE and MAE, in order to select the most suitable model for long-term water quality forecasting and the corresponding grouping of variables. By comparing the actual-predicted value of the degree of fit that is MSE and MAE two major measurement parameters to select the prediction model with the highest accuracy in predicting the composite water quality index WQI. Combining LSTF and water quality prediction field, we can easily see that it can extend to the time prediction limit is more long-term, the stronger the grasp of the relationship between variables, its stronger ability to sense in advance is more suitable for the practical application of the field and the prevention of the development.  
In terms of results, by comparing the degree of fit of the actual-predicted values in Table 7, i.e., the two main measurement parameters MSE and MAE, it is easy to see that Informer is ahead of other models in terms of prediction performance when solving the practical problem of water quality prediction domain + long time series input, and also has better performance than other models in the face of different sets of variables, while Informer -S8 is the best prediction case among them (MSE = 0.2455, MAE = 0.2449). In solving the LSTF Chaohu Lake water quality prediction problem, the model effect Informer > FEDformer > MLP > Transformer > RNN > LSTM, so in this particular area of long-term water quality prediction, Informer has a better adaptability and use prospects, can improve the accuracy of WQI prediction, which is conducive to better management to improve Water quality, effective pollution prevention.  
**Author Contributions:** Conceptualization, Y.Z. ([PERSON]); data curation, P.W. and Z.X.; formal analysis, Z.X.; funding acquisition, Y.W. and Y.Z. ([PERSON]); methodology, S.Y.; resources, Y.W. and Y.Z. ([PERSON]); software, P.W.; supervision, Y.Z. ([PERSON]) and Z.X.; validation, S.Y. and Y.Z. ([PERSON]).; writing--original draft, S.Y.; writing--review and editing, S.Y. and Z.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Anhui agricultural ecological and environmental protection and quality safety industrial technology system grant number 22803029.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank the Beidou Precision Agricultural Information Engineering Laboratory of Anhui Province for supporting us to complete this research. And we sincerely thank [PERSON] from University of Science and Technology of China for his valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.