aiWATERS: An Artificial Intelligence Framework for the Water Sector

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Vekaria, Darshan

Chairs dc:contributor.committeechair

• Sinha, Sunil Kumar
• Ramakrishnan, Narendran

Committee member dc:contributor.committeemember

• Karpatne, Anuj

• Artificial Intelligence
• Water Sector
• Decision Support

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37480

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115799