Abstract
Abstract In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency, effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
