Integrating Conversational AI Agents for Enhanced Water Quality Analytics: Development of a Novel Data Expert System

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Authors

Gabriel Michael Vald , Muhammed Yusuf Sermet, Jerry Mount, Samrat Shrestha, Dinesh Jackson Samuel, David Cwiertny, Ibrahim Demir

Abstract

Despite advancements in environmental monitoring, the gap between data collection and user-friendly data interpretation remains a significant challenge, especially in the domain of water quality management. This paper introduces the Artificial Intelligence Data Expert (AI-DE), a novel data analytics system that is designed to facilitate on-demand analysis of time-series sensor data related to water quality using natural language queries. The AI-DE leverages features of ChatGPT, including Named Entity Recognition, geocoding, and sentiment analysis, to enable intuitive and natural language-based data analysis. This system transformation allows for immediate, ad-hoc querying and interpretation of environmental data, tailored to the needs of diverse user groups. Key features include chat controls that customize user interaction, a chat bypass enabling seamless integration with an integrated information system, and a data interpretation mode for detailed analysis. The AI-DE enhances user engagement and comprehension of water quality data, thereby supporting informed decisions and actions for environmental management. The AI-DE represents a step forward in increasing access to complex environmental data through conversational AI technologies.

DOI

https://doi.org/10.31223/X51997

Subjects

Artificial Intelligence and Robotics, Civil Engineering, Databases and Information Systems, Environmental Engineering, Hydrology, Software Engineering

Keywords

water quality, Time series data, sensor network, data expert, natural language processing, Artificial Intelligence, large language models

Dates

Published: 2024-06-01 01:33

Last Updated: 2024-06-01 08:33

License

CC BY Attribution 4.0 International