# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 5 Results
### Comparative Analysis of ML Methods
#### 5.1.2 Applications of Unsupervised ML Techniques in the Optimization of Treatment Processes
Figure 12 compares the performance of six unsupervised learning algorithms used in wastewater treatment applications. Each algorithm is indicated by a colored line. The chart portrays a clear image of which algorithms excel in specific areas and gives an overall comparison of all six algorithms across all performance measures. \begin{table}
\begin{tabular}{c c c c} \hline
**Technique/Model** & **Application** & **Key Parameters/Output** & **References** \\ \hline Principal Component & Dimensionality reduction & MLSS, pressure, effluent quality & \\ Analysis (PCA) & and feature selection & parameters & [9; 39; 43] \\ \hline t-distributed Stochastic & Nonlinear dimensionality & Process dynamics, pattern & \\ Neighbor Embedding & reduction for data & visualization, anomaly detection & [42; 50] \\ (t-SNE) & visualization & & \\ \hline K-means Clustering & Water quality classification & Water quality indicators, inlet & \\  & and grouping & water classification & [103] \\ \hline DBSCAN & Density-based clustering & Water quality indicators, outlier & \\  & with noise and outlier & detection, noise isolation & [103] \\ \hline AGNES (Agglomerative & Hierarchical agglomerative & Water quality indicators, & \\ Nesting) & clustering & dendrogram representation, & [103] \\ \hline Fuzzy Clustering + PCA & Pattern identification in & \\  & membrane bioreactor & Membrane fouling trends, & \\  & processes & process pattern recognition & [43] \\ \hline Autoencoders & Anomaly detection and & Process anomalies, predictive & \\  & feature extraction & maintenance, and data & [84] \\  & compression & & \\ \hline \end{tabular}
\end{table} 
Table 10: Unsupervised machine learning techniques for wastewater treatment processes. Figure 12: Comparing unsupervised learning techniques used in wastewater prediction across six key criteria. The interpretability dimension is led by AGNES, which obtains a perfect score. AGNES provides maximum interpretability through the hierarchical dendrogram structure it produces. This clearly shows how the data points are progressively grouped into clusters. The tree-like visualization allows users to understand exactly how clustering decisions are being made at each level. Fuzzy clustering uses a key property that allows for strong interpretability: it provides membership degrees that indicate how strongly each data point belongs to different clusters. This property allows for the formulation of linguistic explanations through fuzzy rules. Good interpretability is achieved by t-SNE, primarily because of its visualization capabilities, which allow intricate high-dimensional relationships to be rendered visible in two or three dimensions. K-means, DBSCAN, and PCA all achieve a moderate level of performance. K-means provides easily understandable cluster centroids, DBSCAN presents a clear way of understanding point classifications, and PCA provides principal components that are easy to interpret. However, none of them performs as well as hierarchical methods when it comes to providing explicit decision pathways. Scalability represents the primary strength of K-means algorithms. Its time complexity is linear with regard to the number of data points and clusters. PCA works well with good scalability properties, efficiently processing high-dimensional sensor data from wastewater treatment systems. K-means is the best clustering algorithm for big data. The reason for K-means' success is its simplicity and speed. K-means can be efficiently run on large amounts of data and is easily parallelizable. DBSCAN is able to cluster data of arbitrary shape and is robust to outliers. But when compared to K-means, DBSCAN is slower and not as good for big data. Noisy or messy data are not a problem for DBSCAN, which identifies and isolates outliers quite well, allowing the clustering process to take place without interference from these problematic observations. Compared with DBSCAN, fuzzy clustering and PCA handle messy data and noise far better than AGNES or K-means, which are both pretty sensitive to messy data and outliers.