Machine learning solutions in sewer systems: a bibliometric analysis

The use of machine learning solutions has been rising recently, and the water domain is reaping several benefits from its application. However, there is still room in the literature regarding machine learning applied to sewer systems. In this article, we study applied solutions to the predictive problem of four factors in the sewer: pipe defects, sedimentation, and failure and blockage events. Even with the number of publications available to solve each problem, there is still a need for improvement. This article aims to identify existing literature gaps through a bibliometric analysis based on data extracted from Scopus and Web of Science. Results show an increasing trend in published papers studying the domain and identify different knowledge gaps within the literature related to the correct use of data, the need for models capable of generalization, and the identification of novel techniques to be studied in the future.