# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 3 Materials and Methods  
### Methodology Used for Machine Learning Models Applied in Wastewater Treatment Processes  
The documentation phase began by identifying 300 works. To make the selection process relatively straightforward, the Litmaps tool was used for mapping and sorting academic literature (Figure 2). Various criteria and keyword combinations were specified, such as \"wastewater + machine learning\", \"wastewater + deep learning\", and \"wastewater + artificial intelligence\". The starting year of papers was selected as 2010. Figure 2: Different search criteria for article selection are used in the Litmaps platform. (**a**) Search interface showing ‘wastewater + deep learning’ keyword combination. (**b**) Search interface showing ‘wastewater + machine learning’ keyword combination. (**c**) Search interface showing ‘wastewater + AI’ keyword combination. Figure 3 graphs the connections formed through citations over time between research articles. Each point on the graph represents a distinct paper. They are arranged horizontally from left to right in order of recency; those placed to the left are the oldest, and those on the right are the newest. Vertically on the graph, the papers cited the most often (and which, therefore, can be inferred as being the most influential or at least the most recognizable within certain scholarly circles) are placed higher. Each dot size reflects the influence of a given paper. Larger dots represent works that have been cited more often. The lines between the dots represent citation relationships; when one paper references another, a line is drawn between the two. There is usually a significant amount of ongoing research activity in denser parts of the graph, where the dots are closely packed and heavily connected. These parts of the graph can often be interpreted as active fields or subfields where researchers frequently reference each other's work. Figure 3 shows how densely interconnected papers are in the citation connectivity map, focusing on the extent to which these papers mutually reference one another. When looking at how interconnected these papers are, one sees that some are so interconnected that, when mapped out, they practically encompass a whole citation network unto themselves. Moreover, there is a network of significance: a collection of the most influential papers in a field and the extent to which they form a citation web. Figure 4 is another citation network, but it shows explicitly age-adjusted citation influence, meaning it accounts for how recently a paper was published. The vertical axis now represents more citations adjusted for the paper's age, which helps highlight emerging influential work, even if it has not had many years to accumulate raw citations. Figure 3: Graph of citations associated with 300 articles according to the year of publication. A few papers from 2025 are at the top right. They are the most recent and have the highest age-adjusted citation impact, meaning they have gained many citations very quickly compared to others. That suggests those papers have become highly influential quickly, possibly a breakthrough or a trend-setting work in their field. Older foundational works such as [PERSON] (2011) and [PERSON] (2016) remain visible. However, they are positioned much lower now because their citation counts are expected to be higher due to age, so they do not stand out as much under age-adjusted metrics. Figure 5 shows the temporal distribution of the selected works. The adoption of machine learning to engineering is happening much faster now than in the past, and it looks to us that wastewater treatment is part of this very intense area of growth. Perhaps this is because wastewater treatment is a particularly suitable application due to the follllowing:  
* Complex, nonlinear process dynamics
* Large datasets from sensor monitoring
* Need for predictive control and optimization
* Regulatory pressure for improved efficiency  
Table 1 shows the main journals that contain the articles mentioned in Figures 3 and 4.