2026-09-24T11:09:37Zhttps://api.mountainscholar.org/server/oai/request

oai:mountainscholar.org:10217/2417432026-05-21T19:16:45Zcom_10217_100532com_10217_100000com_10217_199888com_10217_100303col_10217_182111col_10217_199889

Vizarreta Luna, Guillermo Alonso, author Conrad, Steven A., advisor Arabi, Mazdak, committee member Grigg, Neil, committee member Kennan, Alan, committee member 2025-09-01T10:41:59Z 2025-09-01T10:41:59Z 2025 VizarretaLuna_colostate_0053N_19037.pdf https://hdl.handle.net/10217/241743 https://doi.org/10.25675/3.02063 The assumption that natural systems oscillate within a stationary range of variability has traditionally guided water system management and allowed water utilities to experience steady state operations. These steady state operations are viewed as the 'normal state' of the water system. However, non-stationarity events such as wildfires, droughts, and floods shift water systems to new 'states' that negatively impact water quality and complicate water treatment decision-making and performance. Many water system managers do not account for these variations systemically and instead respond reactively as watersheds shift from perceived normal states. To enhance their operational resilience and develop adaptive and robust methodologies, water utilities must gain knowledge of non-stationarity states. Artificial Intelligence (AI) and its subset, Machine Learning (ML), are emerging as key tools for addressing the impacts of non-stationarity events on water system operations. This thesis responds to this gap by investigating how AI and its subset, ML, are emerging as key tools for addressing the impacts of non-stationarity events on water system operations. It provides a review of how AI supports decision-making in drinking water treatment systems when non-stationary water quality states occur due to perturbations. This study provides a summary and observations on: (1) Understanding the boundary influences on water quality due to non-stationarity events and their implications for drinking water treatment processes. (2) Exploring how AI/ML methods inform stationarity and non-stationarity system state patterns. (3) Applying AI/ML models to develop a TOC predictive tool and assess their potential use to address non-stationarity water quality states. born digital masters theses English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. decision making stationarity water systems non stationarity artificial intelligence water quality AI/ML tools for early decision making in water system operations: managing non-stationarity water quality Text Master of Science (M.S.) Masters Systems Engineering Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access