# 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  
Grouping data into various categories is what clustering is all about; it answers the question of 'what' by showing the different kinds of data in a dataset. But then, to understand what kinds of data, we need to look at the structure of the data to figure out 'why' it is grouped in these ways. Models based on deep learning, like convolutional neural networks and recurrent neural networks, can discern patterns and detect anomalous behavior in very large quantities of unlabeled data. This capability makes them potentially valuable for predicting water quality. In many situations, the data cannot be pre-classified [39]. Methods such as principal component analysis help clarify intricate datasets by identifying the most significant underlying patterns. They simplify complex data, reducing it to the most meaningful core elements [9]. In the work by [PERSON] et al. [85], three clustering algorithms were tested to automatically classify inlet water quality without any pre-labeled examples. The algorithms applied to the water samples were K-means, DBSCAN, and AGNES. Every algorithm possesses unique strengths. K-means performed the best; it achieved an accuracy of 94.7% in grouping similar water types and accurately identified the ideal number of clusters. The density-based algorithm DBSCAN was most effective at identifying outliers and other unusual data patterns; it often achieved perfect results but struggled when the data were particularly noisy or complex. The hierarchical algorithm AGNES used a tree-like strategy that let the engineers visually discern the natural grouping patterns in the data, achieving an accuracy rate of 95.5% [103]. The study validated that these unsupervised techniques can efficiently categorize water quality information without human direction, providing automated and valuable tools for engineers' decisions about water treatment [103]. This is especially relevant when we have very little labeled data or are mining the sorts of new environmental patterns that would justify using unsupervised learning. Clustering and dimensionality reduction are essential in unsupervised learning for making prediction models work with the kinds of data found in wastewater treatments. The prediction models must often deal with not just a significant amount of data (high volume) but also a significant amount of variables (high dimensionality). However, it is possible to identify patterns and trends in wastewater data using clustering techniques, such as fuzzy clustering. Techniques for dimensional reduction, such as PCA and t-SNE, make the intricate data from wastewater easier to comprehend and manipulate. They take the convoluted, multi-dimensional data and turn it into something not nearly as hard to digest, without losing significant relationships in the data [104]. t-SNE serves operators very well for one specific purpose: helping them create visual maps of the data that reveal patterns and unusual behavior. PCA plays a different, though equally valuable, role. It tells us which factors we ought to focus on if we want to make accurate predictions. For instance, it can identify whether the operators should be most concerned with membrane performance, the amount of ammonia, or the concentration of nitrogen if they are trying to predict how well the treatment is going to work [43; 105]. Both methods essentially simplify the complex nature of wastewater treatment data, making it more manageable and meaningful for informed decision-making. Table 10 presents ways of using unsupervised machine learning methods that do not require any kind of labeled training data. Because these methods focus on uncovering hidden structures and relationships in datasets, they are well-suited for exploratory dataanalysis, process monitoring, and anomaly detection in the context of wastewater treatment. These methods are essential when the kinds of labels that enable supervised learning are either unavailable or prohibitively expensive.