WaterCopilot: an AI-driven virtual assistant for water management
Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited realtime access, and the complexity of integrating diverse information sources. This paper presents WaterCopilot—an AI-driven virtual assistant developed through collaboration between the In...
| Autores principales: | , , , , |
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| Formato: | Preprint |
| Lenguaje: | Inglés |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/180263 |
| _version_ | 1855521340821340160 |
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| author | Vickneswaran, Keerththanan Garcia Andarcia, Mariangel Retief, H. Dickens, Chris Silva, Paulo |
| author_browse | Dickens, Chris Garcia Andarcia, Mariangel Retief, H. Silva, Paulo Vickneswaran, Keerththanan |
| author_facet | Vickneswaran, Keerththanan Garcia Andarcia, Mariangel Retief, H. Dickens, Chris Silva, Paulo |
| author_sort | Vickneswaran, Keerththanan |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited realtime 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 decisionmaking and strengthen water security in complex river basins. |
| format | Preprint |
| id | CGSpace180263 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | CGSpace1802632026-01-21T04:29:28Z WaterCopilot: an AI-driven virtual assistant for water management Vickneswaran, Keerththanan Garcia Andarcia, Mariangel Retief, H. Dickens, Chris Silva, Paulo artificial intelligence water management digital twins natural language processing large language models river basins Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited realtime 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 decisionmaking and strengthen water security in complex river basins. 2026-01-13 2026-01-21T03:31:55Z 2026-01-21T03:31:55Z Preprint https://hdl.handle.net/10568/180263 en Open Access Vickneswaran, K.; Garcia Andarcia, M.; Retief, H.; Dickens, C.; Silva, P. 2026. WaterCopilot: an AI-driven virtual assistant for water management. arXiv, 15p. doi: https://doi.org/10.48550/arXiv.2601.08559 |
| spellingShingle | artificial intelligence water management digital twins natural language processing large language models river basins Vickneswaran, Keerththanan Garcia Andarcia, Mariangel Retief, H. Dickens, Chris Silva, Paulo WaterCopilot: an AI-driven virtual assistant for water management |
| title | WaterCopilot: an AI-driven virtual assistant for water management |
| title_full | WaterCopilot: an AI-driven virtual assistant for water management |
| title_fullStr | WaterCopilot: an AI-driven virtual assistant for water management |
| title_full_unstemmed | WaterCopilot: an AI-driven virtual assistant for water management |
| title_short | WaterCopilot: an AI-driven virtual assistant for water management |
| title_sort | watercopilot an ai driven virtual assistant for water management |
| topic | artificial intelligence water management digital twins natural language processing large language models river basins |
| url | https://hdl.handle.net/10568/180263 |
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