aiWATERS: An Artificial Intelligence Framework for the Water Sector
| dc.contributor.author | Vekaria, Darshan | en |
| dc.contributor.committeechair | Sinha, Sunil Kumar | en |
| dc.contributor.committeechair | Ramakrishnan, Narendran | en |
| dc.contributor.committeemember | Karpatne, Anuj | en |
| dc.contributor.department | Computer Science and Applications | en |
| dc.date.accessioned | 2023-07-21T08:00:12Z | en |
| dc.date.available | 2023-07-21T08:00:12Z | en |
| dc.date.issued | 2023-07-20 | en |
| dc.description.abstract | The ubiquity of Artificial Intelligence (AI) and Machine Learning (ML) applications has led to their widespread adoption across diverse domains like education, self-driving cars, healthcare, and more. AI is making its way into the industry, beyond research and academia. Concurrently, the water sector is undergoing a digital transformation, driven by challenges such as water demand forecasting, wastewater treatment, asset maintenance and management, and water quality assessment. Water utilities are at different stages in their journey of digital transformation, and its decision-makers, who are non-expert stakeholders in AI applications, must understand the technology to make informed decisions. The non-expert stakeholders should know that while AI has numerous benefits to offer, there are also many challenges related to data, model development, knowledge integration, and ethical concerns that should be considered before implementing it for real-world applications. Civil engineering is a licensed profession where critical decision-making is involved. Failure of critical decisions by civil engineers may put their license at risk, and therefore trust in any decision-support technology is crucial for its acceptance in real-world applications. This research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) to facilitate the successful application of AI in the water sector. Based on this framework, we conduct pilot interviews and surveys with various small, medium, and large water utilities to capture their current state of AI implementation and identify the challenges faced by them. The research findings reveal that most of the water utilities are at an early stage of implementing AI as they face concerns regarding the blackbox nature, trustworthiness, and sustainability of AI technology in their system. The aiWATERS framework is intended to help the utilities navigate through these issues in their journey of digital transformation. | en |
| dc.description.abstractgeneral | The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) in various industries like education, self-driving cars, healthcare, and more has spurred interest in its potential application in the water sector. As the water sector undergoes a digital transformation to address challenges such as water demand forecasting, wastewater treatment, asset management, and water quality assessment, water utilities need to understand the benefits and challenges of AI technology. Automating water sector operations through AI involves high risk as it has a huge ecological, economic, and sociological impact on society. Water utilities are non-expert end users of AI and they should be aware of its challenges such as data management, model development, domain knowledge integration, and ethical concerns when implementing AI for real-world applications. To address these challenges, this research proposes a framework called aiWATERS (Artificial Intelligence for the Water Sector) to help water utilities successfully apply AI technology in their system. We conduct pilot interviews and surveys with small, medium, and large water utilities across the United States to capture their current AI practices and challenges. The research results led us to find that water utilities are still at an early stage of adopting AI in their system and are faced with issues such as blackbox nature of the technology, its trustworthiness for real-world application, and sustainability at the utilities. We believe that aiWATERS will serve as a relevant guide for water utilities and will help them overcome current AI-based challenges. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:37480 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/115799 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Water Sector | en |
| dc.subject | Decision Support | en |
| dc.title | aiWATERS: An Artificial Intelligence Framework for the Water Sector | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |