# 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 \hline
**Technique/Model** & **Application** & **Key Parameters/Output** & **References** \\ \hline \multicolumn{4}{c}{Support Vector Methods} \\ \hline Support Vector Machines & Effluent quality forecasting, & TSS, nutrient levels, & [43; 44; 46; 47; 76; 77] \\ (SVMs) & treatment optimization & classification & \\ \hline Support Vector Regression & Regression-based water & Continuous water quality & [24; 29; 43] \\ (SVR) & quality prediction & parameters & \\ \hline \multicolumn{4}{c}{Tree-based and Ensemble Models} \\ \hline Random Forest (RF) & Feature selection, membrane & COD, BOD5, MBR & \\  & fouling prediction & performance, feature & [8; 18; 24; 69; 71; 76; 77; 92] \\ \hline XGBoost & Ensemble gradient boosting & COD, energy consumption, & \\  & for effluent prediction & ensemble predictions & [30; 69; 75; 77; 92] \\ \hline Tree-based Machine Learning & Decision trees for pollutant & Cr(VI) removal, sludge output & \\  & removal optimization & prediction & [74; 95] \\ \hline Ensemble Learning Methods & Combined multiple models & TDS, EC, salinity, improved & \\  & for enhanced accuracy & robustness & [28; 58; 96] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Traditional ML models are applied in wastewater treatment optimization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique/Model** & **Application** & **Key Parameters/Output** & **References** \\ \hline \hline \multicolumn{4}{c}{Neural Networks} \\ Artificial Neural Networks & Predict WWTP performance, effluent quality, and odor concentration & COD, BOD5, TSS, NH3-N, TP, odor emissions & [9; 27; 39; 56; 76; 79; 80; 89; 97] \\ \hline Recurrent Neural Networks (RNN) & Temporal pattern recognition in treatment data & NH3-N, BOD5, TP, time-series parameters & [9; 33; 39; 49; 60; 80] \\ \hline Long Short-Term Memory (LSTM) & Time-series prediction, COD modeling, N\({}_{2}\)O emissions & COD, BOD5, TP, TN, N\({}_{2}\)O emissions, temporal patterns & [9; 29; 33; 60; 75; 80; 92] \\ \hline Bidirectional LSTM (BiLSTM) & Enhanced time-series modeling with past/future context & Groundwater levels, N\({}_{2}\)O emissions & [28; 41; 68] \\ \hline Deep Recurrent Neural Networks & Advanced RNN architectures & Dissolved oxygen concentration & [90] \\ \hline Convolutional Neural Networks (CNN) & pH prediction, water quality classification, microplastic detection & pH values, water quality patterns, microplastic classification & [5; 8; 11; 29; 41; 42; 49] \\ \hline \multicolumn{4}{c}{Advanced Deep Learning Architectures} \\ Graph Neural Networks (GNN) & Node-level capsule networks for effluent prediction & COD, BOD, TSS using graph structures & [50] \\ \hline Attention Transformers & Advanced sequence modeling for water quality & Water and environmental parameters & [98] \\ \hline \multicolumn{4}{c}{Efficient attention} \\ Sparse Attention Transformers & mechanisms for large-scale data & Water and environmental parameters & [98] \\ \hline Temporal Fusion & Treatment efficiency & BOD5, COD & [12] \\ Transformers & enhancing & \\ \hline Deep Belief Networks & Hierarchical feature learning & Sludge bulking monitoring, process dynamics & [47; 50] \\ \hline \multicolumn{4}{c}{Hybrid Deep Learning Approaches} \\ Hybrid CNN-LSTM Models & Combined spatial-temporal water quality prediction & DO, temperature, turbidity, multi-dimensional data & [29; 67] \\ \hline LSTM-GRU Hybrid & Operational control strategy & Effluent COD, TN, BOD operational parameters & [24; 97] \\ \hline PLO-CNN-BiLSTM-Attention & Advanced N\({}_{2}\)O emission prediction with optimization & N\({}_{2}\)O emissions with symmetry considerations & [41] \\ \hline CNN-MKRBFNN-SSA & Multi-kernel RBF with swarm optimization & COD, BOD, TSS effluent parameters & [11] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deep learning models applied in wastewater treatment optimization.