Application of Large Language Models in Developing Conversational Agents  for Water Quality Education, Communication and Operations

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


The rapid advancement of Large Language Models (LLMs), such as ChatGPT, has opened new horizons in the field of Artificial Intelligence (AI), revolutionizing the way we can engage with and disseminate complex information. This paper presents an innovative application of ChatGPT in the domain of Water Quality (WQ) management, through the development of an AI Hub. The Hub encompasses a suite of conversational agents, each designed to address different aspects of water quality management, including nitrogen pollution, local water quality issues, and actionable planning for water conservation. These agents utilize the advanced natural language processing capabilities of ChatGPT, complemented with water quality-related data, to provide users with accurate, up-to-date, and contextually relevant information. The objective is to empower communities with the knowledge necessary to understand and address water quality challenges effectively. Our comprehensive evaluation of these agents demonstrates their proficiency in delivering valuable insights, with an overall performance accuracy exceeding 89%. This paper underscores the potential of AI-enabled platforms in enhancing public understanding and engagement in environmental conservation efforts. By bridging the gap between complex environmental data and public awareness, the AI Hub sets a precedent for the application of AI in sustainable environmental management.





water quality, intelligent systems, Artificial intelligence, Large Language Models, ChatGPT, chatbots, operational agents


Published: 2024-05-01 06:33

Last Updated: 2024-05-01 13:33


CC BY Attribution 4.0 International