Aging infrastructure, along with the escalating occurrence and severity of extreme weather events linked to climate change, and the growing demands of an increasing population, have placed significant strains on wastewater and stormwater systems. Consequently, there has been a rise in instances of Combined and Sanitary Sewer Overflows (CSOs and SSOs), among other related problems. These challenges have intensified the impact of sewershed issues on both society and the environment. Fortunately, recent advancements in technology, such as sophisticated sensor technologies, more powerful processors, and advanced mathematical modeling techniques, have opened up new possibilities for developing intelligent water systems in the United States that are capable of making well-informed, data driven decisions. While the technological capabilities of these tools are advancing, their application in the water sector is limited and often siloed. Water utilities face a variety of challenges related to digitalization of sewershed management and require a more structured approach for their digital transformation. This research aims to present a comprehensive framework called iWISE (Intelligent Water Infrastructure Systems Engineering) that will serve as a blueprint to facilitate the implementation of Intelligent Sewersheds for water utilities across the country. The proposed framework will focus on enhancing our understanding of various aspects, including system of systems thinking, data management, modeling techniques, decision-making processes, and service delivery, in order to adopt a more intelligent and efficient approach to managing sewersheds. This framework was piloted with small, medium and large scale utilities to capture feedback on the proposed building blocks from a real world perspective and the findings from these interviews reveal that most utilities are in the preliminary stages of intelligent water systems implementation, and is more common among large utilities as compared to their small and medium counterparts. / Master of Science / Water infrastructure in the United States is facing several challenges like the deterioration of infrastructure with time, increasing extreme weather events like flooding due to climate change, and growing population demands. These issues are putting a lot of pressure on wastewater and stormwater systems, leading to more instances of Combined and Sanitary Sewer Overflows (CSOs and SSOs) and other related problems that have significant negative impacts on both the society and the natural environment. Fortunately, recent advancements in technology like remote sensing, internet of things, increased computing power and advanced data analysis tools like artificial intelligence and machine learning, have provided opportunities for water utilities to improve their sewershed management practices. This research introduces a comprehensive framework called iWISE (Intelligent Water Infrastructure Systems Engineering) to help implement the new technologies and practices available for water utilities nationwide to improve the efficiency and reliability of sewershed management.
The framework focuses on improving our understanding of different aspects like how a sewershed and its components are defined, collecting and managing data parameters, modeling techniques, decision-making, and service delivery. The framework was piloted with small, medium, and large scale utilities to get real-world feedback. The findings showed that most utilities are in the early stages of adopting intelligent water systems, and larger utilities are more likely to use these technologies compared to smaller ones.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/117302 |
| Date | 04 January 2024 |
| Creators | Dadiala, Rhea |
| Contributors | Environmental Science and Engineering, Sinha, Sunil Kumar, Little, John C., Thompson, Ken |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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