# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 5 Results
### Comparative Analysis of ML Methods
#### 5.1.1 Applications of Supervised ML Techniques in the Optimization of Treatment Processes
\begin{table}
\begin{tabular}{c c c c} \hline
**Technique/Model** & **Application** & **Key Parameters/Output** & **References** \\ \hline \multicolumn{4}{c}{Interpretable and Explainable Models} \\ \hline Explainable AI–SHAP & Model interpretation and feature importance & Water quality parameters, model transparency & [41; 47; 61] \\ \hline Data-driven pH Models & Specialized pH control systems & pH values in raceway reactors & [35] \\ \hline \multicolumn{4}{c}{Industrial Integration Models} \\ \hline Digital twin and SCADA & Real-time monitoring and control systems & System-wide parameters, process automation & [12; 34; 80] \\ \hline Soft Sensors (ANN + Optimization) & Virtual sensing for real-time monitoring & NH4-N, FOS/TAC ratios, process parameters & [5; 76; 89; 100; 101] \\ \hline \multicolumn{4}{c}{Data Processing and Enhancement} \\ \hline Data Amplification & Data augmentation for small datasets & Enhanced dataset size, improved model training & [41] \\ \hline Time-Series Models & Time-series data transformation, Seasonal and temporal pattern prediction & pH prediction, temporal feature extraction & [8; 47; 75] \\ \hline Remote Sensing + ML Integration & Spatial water quality assessment & Agricultural runoff, climate impact analysis & [73] \\ \hline Hyperparameter Optimization & Automated parameter tuning & Model performance optimization & [102] \\ \hline Machine Learning for Energy Prediction & Energy consumption optimization & Energy consumption in WWTPs & [69; 93] \\ \hline Graph Optimization in Deep Learning & Graph-based optimization for industrial applications & Industrial wastewater influent quality & [86] \\ \hline Life Cycle Assessment & Environmental impact modeling & Environmental, economic, social benefits & [51] \\ \hline \end{tabular}
\end{table} 
Table 9: Specialized and application-specific models in wastewater treatment optimization. Table 6 displays some advanced architectures of neural networks that can learn from large datasets. These models have high performance on data that exist in a much larger than normal number of dimensions (high-dimensional data). They can also automatically extract relevant features from this data. However, they require a large amount of training data and computational resources. Intelligent systems that merge neural networks with fuzzy logic or other AI paradigms provide both learning capability and interpretability (Table 7). These models bridge the gap between accuracy and explainability in applications such as wastewater treatment. Table 8 presents optimization algorithms, which are augmented machine learning methods that improve model performance, parameter tuning, and solution quality. They perform an automated search for best configurations and can handle multi-objective scenarios. In Table 9, there are machine learning models that are tailored toward the difficulties faced in the wastewater treatment sector and its industrial integration. These models have been selected with the domain-specific requirements of the wastewater treatment sector in mind: necessities such as interpretability, real-time implementation, and integrable infrastructure. Figure 10 evaluates five distinct models of supervised learning, ANN, SVM, RF, LSTM, and ANFIS, along with five critical dimensions relevant to their deployment in wastewater management. Random forest leads the interpretability dimension, followed closely by hybrid models. Clear decision pathways are formed by these tree-based and rule-based systems. Engineers follow those pathways and validate their use, which is what makes these systems so valuable for regulatory compliance and process troubleshooting. Moderately interpretable, support vector machines enable decision boundaries to be understood in terms of support vectors. In contrast, artificial neural networks and long short-term memory networks work as black boxes with internal representations that are increasingly more complex (and thus, increasingly less understandable) when compared to support vector machines.