# 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
Prediction of energy consumption shows the combination of random forest and XGBoost in ensemble methods based on trees. In comparative studies, XGBoost has yielded the best RMSE performance. The prediction of sludge production demonstrates how ensemble methods outperform simpler models. These methods effectively manage the intricate patterns and variability that make predicting biological treatment processes especially hard. There are endless biological, chemical, and operational factors at play that influence production rates. Hence, it is especially valuable that the ensembles integrate numerous prediction strategies. The ensemble framework combines classification and regression models to predict the generation of hazardous waste. This is a particularly valuable approach when working with imbalanced datasets, which are common in environmental monitoring applications. Such datasets occur when one class is much smaller in number than the other classes. Limitations and Challenges of Implementing ML Models in Applications Dedicated to Optimizing Wastewater Treatment Processes  
There are many limitations and challenges that machine learning faces across all domains when they are applied in the real world. Machine learning has a predicament in the application of predicting groundwater quality. It needs three things, in particular, high-quality data, specialized expertise, and algorithm adaptability to the complexity of the real world [52]. Catalysis is an area where datasets are limited in size, and the complexity of ML models can cause some problems, particularly overfitting and a lack of generalization [18]. The integration of artificial intelligence with membrane bioreactors is limited by data quality problems, like sensor noise and missing values, that affect the accuracy and dependability of the models [43]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application Area** & **Ensemble Method** & \begin{tabular}{c} **Key Models** \\ **Combined** \\ \end{tabular} & **Performance Metric** & **Advantage** \\ \hline Water Quality & Ensemble Deep & RNN + & 97.81\% Accuracy & \begin{tabular}{c} Temporal \\ dependencies + \\ robust feature \\ selection \\ \end{tabular} \\ \hline Energy Consumption & Tree-based Ensemble & \begin{tabular}{c} Random Forest + \\ XGBoost \\ \end{tabular} & \begin{tabular}{c} Best RMSE \\ performance \\ \end{tabular} & \begin{tabular}{c} Superior handling of \\ complex datasets \\ \end{tabular} \\ \hline Sludge Production & Ensemble Structure & \begin{tabular}{c} XGBoost + \\ Random Forest \\ \end{tabular} & \
\begin{tabular}{c} Outperforms simple \\ models \\ \end{tabular} &
\begin{tabular}{c} Manages complex \\ patterns and \\ variability \\ \end{tabular} \\ \hline Hazardous Waste & Classification + & Coupled ensemble & Superior to direct & Handles imbalanced \\ Generation & Regression & models & regression & datasets effectively \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Ensemble learning methods in WWTP prediction. ML models encounter multiple significant problems that restrict their genuine implementation in the real world, especially in crucial tasks like water treatment. The foremost issue is their black box nature; it is quite often impossible to fathom how they achieve their outcomes, which makes it hard to get decision-makers to trust them with processes that really matter [4; 18; 43]. Another big problem is data. ML models require la significant amount of high-quality data to work properly, but this is both expensive and difficult to get hold of in water treatment. When poor data lead to unreliable models, the amount of good data that is either scarce or hard to access becomes even more critical [49; 52; 100]. These models demand a significant amount of computing power and continuous maintenance. Treatment plants that integrate ML into their systems require considerable retrofitting. The plants themselves must be retooled to accommodate the ML component, and smart systems must be integrated with types of hardware and software that are sometimes decades old [52; 101].