## 4 Results and Discussion
### Discussion  
There are six Deep Learning models in this comparison, and, in general, they are divided into two categories: classical models for short-term forecasting, RNN, LSTM, MLP, Transformer, and long sequence time series forecasting models, Informer, and FEDformer. The performance parameters of each model in solving LSTF water quality WQI forecasts for a specific study area was experimentally measured, and we found that all models have excellent forecasting ability, and among them, the LSTF models are basically better than other models, and Informer is the most excellent forecasting model. In addition, we found that different prediction models have different groups of feature variables for the best prediction results in solving LSTF problems, which also proves that DL models do not have the same effect when facing different combinations of feature variables as inputs. The MLP and LSTM have the best prediction performance in the case of S10, while FEDformer and RNN have the best results in the case of S9, while Informer and Transformer have the highest prediction accuracy in the cases of S8 and S7, respectively.  
Based on the field of water quality prediction, Informer has a better prediction performance than fedformer and other models, and its accuracy and seasonal grasp of the actual application area have good performance; for the prediction area, water quality prediction and water quality assessment will be affected by the local natural environment and has its own regional characteristics, so in a particular environment, the best performance of the Deep Learning time series prediction model will also change. In other studies, [PERSON]'s team [33] chose the LSTM model for the comparison of two different regions, Lake Tai and  
Figure 8: Changes in MSE of the model in different variable groupings: (**a**) MSE loss (**b**) MAE loss.  
Figure 7: Observed and predicted time variation of WQI values using the best performance model based on the LSTF algorithm during the test period: (**a**) Informer-S8 (**b**) FEDformer-S9.  
Lake Victoria, and it was the model with the highest prediction accuracy. In the study, and similarly, in the case study of Poyang Lake basin by [PERSON]'s team [22], the VCLM outperformed the LSTM model and achieved a better performance. After that, in [PERSON]'s [34] water quality prediction study of Labuan River, it shows us that most models change in the face of different regional models by comparing them with 12 different ML models. Therefore, the optimal prediction model in a specific study area may not necessarily be able to achieve the migration of other regions, so the water quality prediction assessment model with regional generality is still the main core of exploration now.  
An important factor affecting the prediction effect between the same prediction model is the consideration of cross-influence between variables, placed in the prediction of water quality composite index, WQI, in addition to the characteristic parameters of water quality testing hydrological characteristics (such as flow rate, icing period, and sand content) land use class, land use classification and climate (temperature, precipitation) and other factors of the composite impact. Although these parameters cannot directly affect the water quality, in time series prediction, the more features are considered and the more interrelationships are set, which can help our predictions to be closer to the real environment. This will also give the opportunity for deep learning prediction models to have higher accuracy and provide more modern tools for subsequent research.