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Making waves: The potential of generative AI in water utility operations.
Lina Sela1, Robert B Sowby2, Elad Salomons3
1Maseeh Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, USA.
Water Research
|December 14, 2024
Summary
Artificial intelligence (AI) can enhance water utility operations. Generative AI offers more accessible natural language interactions, overcoming adoption barriers for specialized AI tools in water systems.
Area of Science:
- Environmental Engineering
- Computer Science
- Water Resource Management
Background:
- Water utilities face complex infrastructure and operational challenges.
- Specialized artificial intelligence (AI) aids in data processing and pattern identification for water systems.
- Current AI adoption is limited by usability, accessibility, and trainability issues.
Purpose of the Study:
- To explore emerging AI topics relevant to water utilities.
- To examine challenges and opportunities in deploying AI tools for water systems.
- To present practical AI applications in water system operations.
Main Methods:
- Review of current AI research in water distribution systems engineering.
- Analysis of barriers to AI adoption in the water sector.
- Exploration of Generative AI's potential for enhanced accessibility.
- Case studies of AI integration: data imputation, asset processing, and demand analysis.
Main Results:
- Generative AI promises more intuitive, natural language interactions, increasing AI accessibility.
- Practical AI applications include missing data imputation, asset data processing, and water demand analysis.
- Addressing usability, accessibility, and trainability are key to broader AI adoption.
Conclusions:
- Prioritizing responsible, user-centered AI solutions is crucial for the water research community.
- Building trust, integrating AI into workflows, and ensuring data privacy are essential.
- Strengthening partnerships is vital for advancing water systems research in the AI era.
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