Technology Category
Humans and AI: AI for AccessibilityComputer Vision: Remote Sensing / Geospatial AIPhilosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
WaterCopilot: An AI-Driven Virtual Assistant for Water Management
🤖 AI Summary
This study addresses the challenges of transboundary water governance—stemming from fragmented data, lack of real-time information, and difficulties in integrating heterogeneous sources—by developing an AI-powered virtual assistant based on a Retrieval-Augmented Generation (RAG) architecture enhanced with tool-calling capabilities. The system integrates static policy documents and real-time hydrological data through custom plugins, enabling multilingual interaction, automated computation, visualization, and transparent source tracing. It introduces a novel threshold-triggered early-warning mechanism coupled with a digital twin framework. Deployed on AWS and leveraging Azure AI Search for semantic retrieval, the solution establishes a scalable and reusable AI governance framework. Validation in the Limpopo River Basin yielded a RAGAS overall score of 0.8043 (answer relevance: 0.8571), demonstrating significant improvements in the timeliness and scientific rigor of transboundary water management.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
transboundary river basins
water resource management
fragmented data
real-time access
information integration
Innovation
Methods, ideas, or system contributions that make the work stand out.
Retrieval-Augmented Generation (RAG)
AI-driven virtual assistant
real-time hydrological data integration
Digital Twin
multilingual water governance
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