## 3 BERT Topics and General Bibliometrics  
In this section, a general bibliometric analysis of scientific production will be carried out using all the articles obtained in the Scopus query. In the following sections, article selection will be performed using topic analysis and expert selection. The bibliometric analysis commences with the identification of the most significant sources based on the number of articles published in various scientific journals during a specific time period. Figure 2a presents the results of this analysis. Upon analyzing a total of 1087 documents, it was discovered that there are 362 publication sources, which consist of journals or conference proceedings. The top 10 sources were found to account for 272 documents, equivalent to 25% of the total production. The top three sources identified were _Science of the Total Environment_ (4.7%), _Journal of Hydrology_ (4.5%), and _Water Research_ (3.3%). On the other hand, when we analyze the h-index in Figure 2b, we observe that the three journals remain the most cited. However, there is a change in the ranking order, with _Journal of Hydrology_ obtaining an h-index of 19, _Science of the Total Environment_ of 17, and _Water Research_ of 12. In Figure 3, Bradford's Law is illustrated [28], depicting the publication dynamics for different years. The law segments the distribution of research field literature into three zones: the core zone contains the most important journals, the second zone consists of less cited journals, and the third includes many infrequently cited journals. Bradford's Law aims to identify the 'core' sources by conducting a citation analysis. Journals are sorted by the number of articles contributed, and a 'core' is established at the point where the contribution frequency significantly decreases. This point marks the transition from the core to the next zone and is determined by identifying where the number of articles starts to decline sharply, thereby setting the core's limits. From the graph, we observe that the main sources analyzed in Figure 2 remain consistent. Another important aspect is understanding the evolution of scientific production over time, which is shown in the lower graph of Figure 3. Upon analyzing the graph, we observe that the slope changes significantly, increasing from the year 2019 onward. This change is consistent across the top six journals analyzed. Figure 2: Key journals highlighting the intersection of water management and machine learning, showcasing the most relevant studies in the field. (**a**) Impact measured through the number of documents. (**b**) Impact measured through the H-index. In Figure 4, statistics for prominent authors contributing to the field of water management are showcased. A threshold of at least 8 articles published between 2015 and 2023 was established. In Figure 4a, the 20 authors who fulfilled this criterion are highlighted, with a total of 18 articles published by [PERSON] [29] who leads the pack. [PERSON] [30] follows closely with 15 articles, and 13 articles by [PERSON] [31] ranks third. In Figure 4b, the authors are visually represented based on the number of articles (indicated by the size of the circles) and the number of citations received (denoted by the color of the circles), plotted over time. From 2019 onward, articles began to be published by eleven authors, while three started from 2018, three from 2017, four from 2016, and two from 2015. Additionally, it is worth noting that an active publication record throughout the years is consistently maintained by almost all authors. Figure 3: Prominent Journals at the Intersection of Water Management and Machine Learning: (**a**) Showcasing the Application of Bradford’s Law, and (**b**) Highlighting Trends in Source Growth and Development within the Field. In Figure 5a, a collaboration network graph has been generated, showing the relationships between the top authors in the dataset based on their co-authorship of publications. In the graph, there are four main groups.