WaterCopilot: An AI-Driven Virtual Assistant for Water Management

• URL: http://arxiv.org/abs/2601.08559v1
• Date: Tue, 13 Jan 2026 13:44:00 GMT
• Title: WaterCopilot: An AI-Driven Virtual Assistant for Water Management
• Authors: Keerththanan Vickneswaran, Mariangel Garcia Andarcia, Hugo Retief, Chris Dickens, Paulo Silva,
• Abstract summary: WaterCopilot is an AI-driven virtual assistant developed by the International Water Management Institute (IWMI) and Microsoft Research for the Limpopo River Basin (LRB)<n>WaterCopilot integrates static policy documents and real-time hydrological data via two custom plugins.<n>System features guided interactions (English, Portuguese, French), transparent source referencing, automated calculations, and visualization capabilities.
• Score: 0.44366329731054943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited real-time access, and the complexity of integrating diverse information sources. This paper presents WaterCopilot-an AI-driven virtual assistant developed through collaboration between the International Water Management Institute (IWMI) and Microsoft Research for the Limpopo River Basin (LRB) to bridge these gaps through a unified, interactive platform. Built on Retrieval-Augmented Generation (RAG) and tool-calling architectures, WaterCopilot integrates static policy documents and real-time hydrological data via two custom plugins: the iwmi-doc-plugin, which enables semantic search over indexed documents using Azure AI Search, and the iwmi-api-plugin, which queries live databases to deliver dynamic insights such as environmental-flow alerts, rainfall trends, reservoir levels, water accounting, and irrigation data. The system features guided multilingual interactions (English, Portuguese, French), transparent source referencing, automated calculations, and visualization capabilities. Evaluated using the RAGAS framework, WaterCopilot achieves an overall score of 0.8043, with high answer relevancy (0.8571) and context precision (0.8009). Key innovations include automated threshold-based alerts, integration with the LRB Digital Twin, and a scalable deployment pipeline hosted on AWS. While limitations in processing non-English technical documents and API latency remain, WaterCopilot establishes a replicable AI-augmented framework for enhancing water governance in data-scarce, transboundary contexts. The study demonstrates the potential of this AI assistant to support informed, timely decision-making and strengthen water security in complex river basins.

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