WaterCopilot: An AI-Driven Virtual Assistant for Water Management

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.

Automated river gauge plate reading using a hybrid object detection and generative AI framework in the Limpopo River Basin

Accurate and continuous monitoring of river water levels is essential for flood forecasting, water resource management, and ecological protection. Traditional hydrological observation methods are often limited by manual measurement errors and environmental constraints. This study presents a hybrid framework integrating vision based waterline detection, YOLOv8 pose scale extraction, and large multimodal language models (GPT 4o and Gemini 2.0 Flash) for automated river gauge plate reading. The methodology involves sequential stages of image preprocessing, annotation, waterline detection, scale gap estimation, and numeric reading extraction. Experiments demonstrate that waterline detection achieved high precision of 94.24 percent and an F1 score of 83.64 percent, while scale gap detection provided accurate geometric calibration for subsequent reading extraction. Incorporating scale gap metadata substantially improved the predictive performance of LLMs, with Gemini Stage 2 achieving the highest accuracy, with a mean absolute error of 5.43 cm, root mean square error of 8.58 cm, and R squared of 0.84 under optimal image conditions. Results highlight the sensitivity of LLMs to image quality, with degraded images producing higher errors, and underscore the importance of combining geometric metadata with multimodal artificial intelligence for robust water level estimation. Overall, the proposed approach offers a scalable, efficient, and reliable solution for automated hydrological monitoring, demonstrating potential for real time river gauge digitization and improved water resource management.

Dam Volume Prediction Model Development Using ML Algorithms

Reliable reservoir volume estimates are crucial for water resource management, especially in arid and semi-arid regions. The present study investigates applying three machine learning regression techniques - Gradient Boosting, Random Forest, and ElasticNet to predict key dam performance characteristics of the Loskop Dam in South Africa. The models were trained and validated on a dataset comprising geospatial elevation measurements paired with corresponding reservoir supply capacity values. The best-performing approach was a threshold-based blended model that combined random forest for higher volumes with Ridge regression for lower volumes. This model achieved an RMSE of 4.88 MCM and an R2 of 0.99. These findings highlight the ability of ensemble learning techniques to capture complex relationships in dam datasets and underscore their practical utility for reliable dam performance modelling in real-world water resource management scenarios.