Machine Learning Applied to Water Distribution Networks Issues: A Bibliometric Review


  • H Denakpo Institute of Mathematics and Physics image/svg+xml
  • P Houngue Institute of Mathematics and Physics image/svg+xml
  • T Dagba Ecole Nationale d'Economie Appliquée et de Management
  • J Degila Institute of Mathematics and Physics image/svg+xml



Water Distribution Networks, Machine Learning, Bibliometric study, Bibliometrix


INTRODUCTION: Water Distribution Networks are critical infrastructures that have garnered increasing interest from researchers.

OBJECTIVES: This article conducts a bibliometric analysis to examine trends, the geographical distribution of researchers, hot topics, and international cooperation in using Machine Learning for Water Distribution Networks over the past decade.

METHODS: Using “water distribution” AND (prediction OR “Machine learning” OR “ML” OR detection OR simulation), as search string, 4859 relevant publications have been retrieved from WoS database. After applying the PRISMA method, we retained 2427 documents for analysis with a Bibliometric library programmed in R.

RESULTS: China and the USA are the most productive on the ground, and only one African country appears in this ranking in 14th place. We also identified two ways for future research works, which are: the assessment of water quality and the design of optimisation models.

CONCLUSION: The application of this research in African countries would be fascinating for a better quality of service and efficient management of this resource, which is inaccessible to many African countries.


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How to Cite

Denakpo H, Houngue P, Dagba T, Degila J. Machine Learning Applied to Water Distribution Networks Issues: A Bibliometric Review. EAI Endorsed Trans Energy Web [Internet]. 2024 Mar. 27 [cited 2024 Apr. 21];11. Available from: