# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 5 Results
### Comparative Analysis of ML Methods
#### 5.1.3 Efficiency of Ensemble Methods in Optimizing Wastewater Treatment Processes  
Ensemble methods can increase the efficiency of the treatment of wastewater by using several different models to arrive at a combination that performs better than any single model. This is common in machine learning. The ensemble models often used in deep learning, particularly those that use recurrent neural networks, are very powerful. These models can predict water quality in wastewater treatment plants with an accuracy of 97.81%. What makes these joint models so powerful is their ability to deal with different facets of the data at the same time. They can monitor how things change over time while also zeroing in on the big, important drivers that affect treatment performance. This dual  
Figure 13: Performance metrics and characteristics of unsupervised learning methods. Colors indicate performance levels: red (undesirable), orange (moderate), green (desirable). capability lets them better tackle the inherent messiness of environmental data than the traditional single-model approach [73]. The performance of models that use random forest and XGBoost has been well established. A user of this work would not have to question whether or not either of these models performs better than the other, since XGBoost plainly dominates in terms of both predictive accuracy (as reflected in much lower RMSE values) [69]. Ensemble models have shown their ability in predicting the production of sludge. They are particularly good at fitting highly nonlinear data. Using XGBoost and random forest can make the prediction much better than simple models, almost always winning head-to-head comparison tests. They manage complex patterns and variability in the data much better than simple or even slightly complex models [74]. When predicting the generation of hazardous waste, it is much better to use ensemble models that couple classification and regression. These perform in a superior way over direct regression models. Direct regression models have a tough time handling imbalanced datasets [77]. The operational gain comes primarily from the ensemble methods simply being much more accurate in their predictive capabilities. These models can adapt to various environmental conditions and can also integrate data from different sources, making them a good choice in terms of an optimization tool for real-world wastewater treatment scenarios [69,73]. The radar chart in Figure 14 shows very clearly that ensemble methods outperform single models in each of the wastewater treatment applications. The performance gap is not slight either; ensemble methods achieve an overall accuracy of 97.81% in predicting water quality, while single models only manage 75%. Energy consumption forecasting is an ensemble method that excels, with 85% effectiveness compared to 65% for individual models like XGBoost or random forest. Again, ensemble methods lead the way when predicting sludge production, with an advantage of 88% versus 70% for complex data pattern handling. Thus, approaches using the ensemble  
Figure 14: Model performance comparison between ensemble methods and single models. paradigm provide a clear advantage for these environmental predictions. When predicting hazardous waste, ensemble methods lead at 82 percent, while individual models are significantly lower at 68 percent. This is especially helpful when dealing with the imbalanced datasets that are the norm in wastewater treatment situations. In Table 11, the water quality forecasting row describes how ensemble deep learning blends recurrent neural networks with random forest algorithms to achieve 97.81% accuracy. RNNs take care of the water quality data when it is arranged in a time-dependent way. In contrast, random forest provides stable features of importance rankings and make solid decisions based on the features they select. Thus, these two methods harmoniously work together on the water quality data.