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Call for Papers: Role of Big Data Analytics for Realizing Intelligent Water Networks

Smart Water welcomes submissions to the thematic series on the role of big data analytics for realizing intelligent water networks.

Conventional means of water monitoring and customer feedback are no longer adequate in the era of the digital information age. Increasing water customer and utility professional expectations for information at their fingertips is creating growing pressure on the utility sector to transition to digital technologies.

An intelligent water grid refers to the integration and remote communication of information via supporting technologies such as meters, sensors, and computerized controls that continuously and remotely monitor the water flow and usage within the distribution system and customer connection. The introduction and advancement of these advanced enabling technologies has allowed an expanded capacity to monitor many different parameters. For water distribution this includes pressure, quality, temperature, flow rates, and leaks, to name a few. 

However, intelligent water networks alone add little value without the accompanying big data informatics and reporting to customers and utility professionals. Informatics applying statistical, mathematical, machine learning and rule-based approaches can be used to provide vital information on water usage at hourly, minute or second intervals. Such information is powerful for water service providers, government water departments and customers actively seeking to better manage water usage and wastage. Moreover, acknowledgement of water-energy connections is providing a business case for the integration of water and energy multi-utility services provision.

The Guest Editor is looking for papers that demonstrate how intelligent water network technologies and big data analytics can be used to enhance urban water networks. The research work should demonstrate novel concepts and applications within one or more aspects of the entire network from supply through to distribution and the customer/end-user. Operational, forecasting, customer, finance, economic, engineering, social, science, and asset management dimensions are encouraged. The use of advanced data mining and pattern recognition techniques for providing novel ways to manage urban water resources are requested. Papers that unpack the interaction between water-energy data are particularly encouraged.

  • Smart metering
  • Digital metering
  • Water demand management
  • Intelligent water networks
  • Digital utility transformation
  • Data mining and pattern recognition

Submission Instructions

Before submitting your manuscript, please ensure you have carefully read the submission guidelines for Smart Water. The complete manuscript should be submitted through the Smart Water submission system. To ensure that you submit to the correct special issue please select the appropriate special issue in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on the Role of Big Data Analytics for Realizing Intelligent Water Networks. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

Lead Guest Editor

Prof. Rodney A. Stewart, School of Engineering & Built Environment, Cities Research Institute (CRI), Griffith University, Queensland, Australia

Submissions will also benefit from the usual advantages of open access publication:

  • Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
  • High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article
  • No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
  • Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed

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