{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129451"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129451","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven approaches for residential water end-use classification and sustainable urban water management","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Heydari, Zahra"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Stillwell, Ashlynn S","Guest, Jeremy","Tessum, Christopher","Cominola, Andrea"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-28","date_published":"2025-04-28","updated_at":"2026-07-22T22:25:05Z","subjects":["water sustainability","smart water metering","machine learning","water demand management"],"languages":["en","eng"],"rights":["Copyright 2025 Zahra Heydari"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129451","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Stillwell, Ashlynn S","Guest, Jeremy","Tessum, Christopher","Cominola, Andrea"]},{"key":"dc:creator","label":"Author","values":["Heydari, Zahra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-28","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["water sustainability","smart water metering","machine learning","water demand management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Zahra Heydari"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129451"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Zahra Heydari, accepted the attached license on 2025-04-25 at 13:54.","The student, Zahra Heydari, submitted this Dissertation for approval on 2025-04-25 at 14:11.","This Dissertation was approved for publication on 2025-04-28 at 15:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21987 on 2025-10-19 at 18:19:20","This dissertation utilizes data analysis and machine learning techniques to analyze residential water consumption at an end-use level through non-intrusive load monitoring, focusing on urban water sustainability and supporting the implementation of smart water meters and machine learning techniques to improve water management. The three objectives rely on smart water meter data to disaggregate, classify, and analyze water consumption and extend the availability of labeled data in the residential water literature. Objective 1, classification assessment based on different data resolution, aims to understand how different levels of temporal data resolution can impact the accuracy of end-use classification and provide insight into the efficient resolution needed for accurate water consumption behavior analysis. Objective 2 builds on the existing labeled dataset collected through Objective 1 to create a larger synthetic dataset and compare different classification models and identify the most efficient model for water end-use classification based on accuracy and computation time. This objective compares the performance of various machine learning algorithms and identifies the most accurate and computationally efficient model for water end-use classification. Finally, Objective 3 investigates the potential insights that smart water systems can provide once end use-level disaggregation is achieved, with a particular focus on stagnation time as a new metric for analyzing residential water consumption. In this objective, high-resolution water-use data are leveraged to quantify stagnation time (i.e., the duration an appliance remains unused) across different fixtures, offering insights into usage consistency, behavioral flexibility, and the early detection of anomalies, such as leaks. Beyond conservation, stagnation time also has implications for premise plumbing water quality, since extended stagnation periods can contribute to microbial regrowth and chemical degradation. By integrating stagnation time analysis with traditional flow-based monitoring, this objective supports demand-side water management strategies and conservation interventions, and can be broadly applied as smart meter deployments expand and more labeled data become available. The overall findings contribute to a deeper understanding of how smart data collection can enhance sustainable water management, supporting more efficient resource allocation, proactive system maintenance, and informed policy development for urban water systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven approaches for residential water end-use classification and sustainable urban water management"]}]}],"canonical_facts":{"dc:contributor":["Stillwell, Ashlynn S","Guest, Jeremy","Tessum, Christopher","Cominola, Andrea"],"dc:creator":["Heydari, Zahra"],"dc:date":["2025-04-28","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Zahra Heydari, accepted the attached license on 2025-04-25 at 13:54.","The student, Zahra Heydari, submitted this Dissertation for approval on 2025-04-25 at 14:11.","This Dissertation was approved for publication on 2025-04-28 at 15:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21987 on 2025-10-19 at 18:19:20","This dissertation utilizes data analysis and machine learning techniques to analyze residential water consumption at an end-use level through non-intrusive load monitoring, focusing on urban water sustainability and supporting the implementation of smart water meters and machine learning techniques to improve water management. The three objectives rely on smart water meter data to disaggregate, classify, and analyze water consumption and extend the availability of labeled data in the residential water literature. Objective 1, classification assessment based on different data resolution, aims to understand how different levels of temporal data resolution can impact the accuracy of end-use classification and provide insight into the efficient resolution needed for accurate water consumption behavior analysis. Objective 2 builds on the existing labeled dataset collected through Objective 1 to create a larger synthetic dataset and compare different classification models and identify the most efficient model for water end-use classification based on accuracy and computation time. This objective compares the performance of various machine learning algorithms and identifies the most accurate and computationally efficient model for water end-use classification. Finally, Objective 3 investigates the potential insights that smart water systems can provide once end use-level disaggregation is achieved, with a particular focus on stagnation time as a new metric for analyzing residential water consumption. In this objective, high-resolution water-use data are leveraged to quantify stagnation time (i.e., the duration an appliance remains unused) across different fixtures, offering insights into usage consistency, behavioral flexibility, and the early detection of anomalies, such as leaks. Beyond conservation, stagnation time also has implications for premise plumbing water quality, since extended stagnation periods can contribute to microbial regrowth and chemical degradation. By integrating stagnation time analysis with traditional flow-based monitoring, this objective supports demand-side water management strategies and conservation interventions, and can be broadly applied as smart meter deployments expand and more labeled data become available. The overall findings contribute to a deeper understanding of how smart data collection can enhance sustainable water management, supporting more efficient resource allocation, proactive system maintenance, and informed policy development for urban water systems."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129451"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Zahra Heydari"],"dc:subject":["water sustainability","smart water metering","machine learning","water demand management"],"dc:title":["Data-driven approaches for residential water end-use classification and sustainable urban water management"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}