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

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2024-03-27 DOI:10.4108/ew.5567

H. Denakpo, P. Houngue, T. Dagba, J. Degila

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Abstract

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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将机器学习应用于配水管网问题：文献计量学综述

引言：配水管网是重要的基础设施，越来越受到研究人员的关注。目的：本文通过文献计量分析，研究了配水管网的发展趋势、研究人员的地理分布、热门话题以及研究人员的研究成果：本文通过文献计量分析，研究了过去十年中机器学习在配水管网中的应用趋势、研究人员的地理分布、热点话题以及国际合作。方法：使用 "配水管网 "和（预测或 "机器学习 "或 "ML "或检测或模拟）作为检索字符串，从 WoS 数据库中检索到 4859 篇相关出版物。应用 PRISMA 方法后，我们保留了 2427 篇文献，并使用 R 语言编程的文献计量学库进行了分析。结果：中国和美国的实际成果最多，只有一个非洲国家出现在这一排名中，位居第 14 位。我们还确定了未来研究工作的两个方向：水质评估和优化模型设计。结论：这项研究在非洲国家的应用将对提高服务质量和有效管理许多非洲国家无法获取的这一资源大有裨益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.