# 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
t-SNE demonstrates moderate performance in this application; it falls somewhere in the middle. The other methods require more complex parameter tuning and setup decisions. The parameter-free nature of PCA also contributes to its high score as a basic method for dimensionality reduction. However, choosing the optimal components may necessitate some domain expertise. DBSCAN, fuzzy clustering, and t-SNE are all of moderate ease of use in the world of data mining, but they are also of moderate difficulty because they require some carefulness in achieving good results. Each method requires the user to specify certain parameters that can dramatically affect the outcome. DBSCAN requires not only the epsilon value to be carefully determined but also the minimum points; fuzzy clustering requires the selection of a fuzziness parameter that is appropriate for the dataset at hand. t-SNE not only requires a careful selection of perplexity value but is also somewhat sensitive to the choice of certain other hyperparameters. The AGNES algorithm works at a moderate level on this metric. It requires a decision about linkage criteria and distance metrics, as well as a need to determine the optimal number of clusters from the dendrogram, which can be hard to do and is often domain-dependent. Dimensionality reduction is what PCA and t-SNE are meant to do, and they do it well. PCA is good for linear reductions, keeping the important information, and t-SNE is good for the opposite: taking complex, nonlinear patterns and showing them in a way that humans can understand easily. The other algorithms are not really built for this purpose. In terms of accuracy, K-means, DBSCAN, and fuzzy clustering all do a good job, but in different ways. K-means is good at finding what it seems to be finding--clear, round clusters. DBSCAN seems to be able to identify separated, oddly-shaped clusters and can handle varying densities with ease. Fuzzy clustering seems to do a good job at presenting what can be thought of as nuanced results that capture the overlapping operational conditions. AGNES achieves moderate performance on reduced datasets; it does not handle noisy data well. t-SNE and PCA are not good for clustering since they are not designed for that task. t-SNE is designed for visualization, and PCA is for data reduction. They are both better at serving those purposes than they are at clustering. Large-scale wastewater treatment operations benefit the most from K-means. When handling noisy data in which outliers may possibly be important signals, DBSCAN is a better algorithm to use, as it is particularly good at finding even the most unusual-seeming patterns. For smaller datasets where it is necessary to explain the results to stakeholders, AGNES is perfect. PCA and t-SNE are not truly clustering tools--they are better utilized for data preparation and for producing visualizations. In Figure 13, a comprehensive overview of machine learning techniques and models commonly applied in wastewater treatment and water quality analysis can be seen.