# 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
* No records (0) came from registers
* 0 duplicate records were removed (using Litmaps, no duplicate records were selected)
* No records were excluded by automation tools or for other reasons
* Right side (Other Methods):
* 5 records were identified from websites
* No records (0) came from organizations
* 19 records were found through citation searching
2. Screening Section
* Database path:
* 300 records were screened (after removing duplicates)  
Figure 6: PRISMA 2020 flow chart for a systematic literature review on machine learning and deep learning models for wastewater process prediction [64]. * This stage excluded 37 records. These were papers that were not about wastewater and did not mention ML/DL, which we had hoped would be discussed in the types of papers we were including. * 263 reports were sought for retrieval (full texts)
* 53 reports could not be obtained (full texts were inaccessible)
* 210 reports were assessed for eligibility. * Other methods path:
* 24 reports were pursued for retrieval
* 11 reports could not be retrieved
* 13 reports were assessed for eligibility
* Exclusion Reasons (Eligibility Assessment)
* From the database path, reports were excluded for these reasons:
* 68 were not about wastewater prediction
* 29 did not use ML/DL models
* 11 had insufficient validation content
* 12 were duplicate or redundant analyses
* 6 used outdated technology
* 4 had methodological flaws
* Six reports were excluded from other methods (duplicate or redundant analyses). 3. Included Section * In total, the review encompassed 80 articles from databases and registers and 7 through alternative means. * Final articles: 87. Figure 7 shows the citation relationships of 80 academic articles that were selected from a larger set of 300. Publications are organized along the horizontal axis according to their publication dates. The ones on the right are the most recent, and the ones on the left are the oldest. The vertical axis indicates how frequently these publications have been cited, with the ones higher up being referenced more often. This perspective shows a basic downward slope from left to right, which is what you would expect: age should equal citation accumulation; after all, and papers to the right on this line should, in theory, be the least cited, since they are the freshest in the scholarly conversation. Yet there are two papers relatively high on the y-axis, even though their x-values indicate they should be less influential simply because they are newer. Similar to Table 1, Table 2 includes selected journals that feature more than two papers on wastewater treatment processes, as identified from articles that have passed the screening process. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Journal** & **2020** & **2021** & **2022** & **2023** & **2024** & **2025** & **Total** \\ \hline Water & 1 & & 2 & 5 & 10 & 3 & 21 \\ \hline Sustainability & & 1 & 1 & 1 & 4 & & 7 \\ \hline Applied Sciences & & & & & 2 & 3 & 5 \\ \hline \end{tabular}
\end{table} 
Table 2: Distribution of published articles on wastewater treatment in ML context by journal and year (2020–2025).