## 1 Introduction
These representations neglect the context of words within a sentence, potentially hindering the accurate representation of documents. To address these limitations, this article employs a semi-automatic approach for conducting bibliographic analysis, leveraging BERTopic [20]. This method offers a better contextual understanding compared to its predecessors, making it well-suited for modeling topics. In this semi-automatic approach, an initial search is performed using the Scopus database, yielding a collection of abstracts relevant to the inquiry. These abstracts undergo topic modeling utilizing Bidirectional Encoder Representations from Transformers (BERTopic) [21]. Subsequently, an expert validates the primary topics for consistency and selects those deemed pertinent. With the relevant terms from each topic, new Scopus queries are generated, leading to a traditional bibliographic analysis based on the query results, complemented by a clustering analysis grounded in bigrams. Four primary topics within the sphere of water infrastructure integrity and quality are illuminated by the findings: Advancements in Machine Learning for Water Contaminants and Soil Erosion, Forecasting Water Levels, Advanced Leak Detection in Water Infrastructure, and Assessing Infrastructure Integrity alongside Water Quality and Potability. A diverse array of challenges and research opportunities become apparent upon a meticulous exploration of each topic. The outcomes of this review advocate for the potential of machine learning techniques combined with cutting-edge monitoring systems in multiple aspects of water infrastructure and quality. Noteworthy enhancements are emphasized in predicting groundwater contamination, forecasting water levels, detecting and pinpointing infrastructure vulnerabilities and leaks, and gauging and forecasting water quality. These progressions, resulting from the melding of technology and research, facilitate the preservation and sustainable utilization of this paramount resource. Building on these critical developments, we delineate the primary contributions of this article, which not only underscore the significant strides made but also chart a course for future explorations in this domain. The main contributions of the article are:  
* The article conducts a bibliographic analysis of English-language articles from 2015 onward, resulting in a total of 1087 articles, to explore the applications of machine learning (ML) techniques in water infrastructure integrity and quality. * The study utilizes a semi-automatic approach, leveraging BERTopic, for conducting the bibliographic analysis, which enhances the contextual comprehension in topic modeling. * The article also emphasizes the potential of combining ML techniques with cutting-edge monitoring systems in multiple aspects of water infrastructure and quality. * The insights drawn from the analysis highlight the instrumental role of ML in enhancing water infrastructure's integrity and quality, suggesting promising future research directions. The following sections are structured as follows: In Section 2, the process for conducting the bibliographic analysis is described. Sections 3 and 4 provide a detailed bibliographic analysis of the chosen articles. Initially, BERT topics are selected and a general biometric analysis is conducted in Section 3. Subsequently, Section 4 presents a bigram analysis for each selected topic, along with the traditional bibliographic analysis. Future research directions are discussed in Section 5, and finally, Section 6 outlines the conclusions and next steps.