## 6 Conclusions  
In this study, a hybrid methodology has been employed. Firstly, the bidirectional encoder representation from transformers was used to identify topics in article abstracts obtained from Scopus. Subsequently, expert knowledge was relied upon to select relevant topics. This methodology successfully identified four areas of machine learning application in water management: Advancements in Machine Learning for Water Contaminants and Soil Erosion, Forecasting Water Levels, Advanced Leak Detection in Water Networks, and Assessing Water Quality and Potability. Leading journals in this research area have been identified as _Science of the Total Environment_, _Journal of Hydrology_, and _Water Research_. Through the analysis of bigrams and traditional methods, classification models using key techniques such as RF, ANN, SVM, KNN, and convolutional networks have been found crucial for leak detection in water networks. These models focus on various methodologies, yielding promising results with up to 98-99% accuracy, significantly improving leak detection and management. In the topic of water level forecasting, machine learning techniques like SVR, CNN, GTB, and Random Forest have demonstrated their effectiveness in groundwater potential mapping, level prediction, and water quality management. These approaches enhance spatial predictions, address complex terrains, and monitor dissolved organic matter, contributing to sustainable groundwater management, peatland restoration strategies, and water safety amid urbanization and climate change. Machine learning techniques such as CNN, LSTM, and Random Forest have proven successful in the topic of water quality and potability. These methods offer high precision and reduced processing time, enabling better water management, early warning systems, and support for decision-making in sustainable resource management. Machine learning techniques, like Random Forest and XGB, have demonstrated success in predicting groundwater nitrate concentrations and soil erosion susceptibility, as well as estimating heavy metal and nutrient levels in various regions. These models help identify at-risk areas and crucial contributing factors, informing sustainable management plans, soil conservation, and watershed protection. Such data-driven approaches offer valuable insights for addressing natural pollution problems and ensuring water quality. To advance research in Water Infrastructure Integrity and Quality, it is essential to foster interdisciplinary collaboration among experts from various fields, create integrated frameworks that combine diverse machine learning models and techniques, and enhance data sources using remote sensing, hyperspectral imagery, and IoT technologies. Driving innovation through the development of advanced sensors, monitoring systems, and communication technologies will improve strategies for maintaining water infrastructure integrity and quality. Assessing and optimizing model transferability, measuring the impact of predictive models on policy-making and conservation efforts, and raising public awareness and education about challenges related to water infrastructure are crucial. Implementing pilot projects to test and validate new technologies in real-world scenarios and evaluating the socioeconomic and environmental effects of improved strategies will further support research and development. This pursuit of technological advancement in Water Infrastructure Integrity and Quality must also address the challenges and potentials within the field of machine learning itself. While BERTopic enhances contextual understanding in topic modeling, it is not without limitations, such as potential biases in pre-trained models. Future research could delve into identifying these biases and developing strategies to mitigate them, perhaps through the inclusion of Large Language Models (LLMs) which offer diverse data training and nuanced language understanding.