Page 214 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

hydrological workflows, enhancing flood resilience, and enabling data-driven water resource
governance.

Modern river basin management faces dual pressures from climate volatility and anthropogenic
impacts. Traditional hydrological methods, often reliant on manual expertise, fragmented
data, and labor-intensive field surveys, struggle to meet demands for precision and timeliness.
Key challenges span real-time flood risk forecasting in complex river systems, high-resolution
sediment transport modelling, adaptive water allocation balancing ecological and economic
needs, and cost-effective infrastructure monitoring across diverse geographical conditions.

The hydrology large model enables an intelligent transformation and upgrade of water
resource management based on its multi-modal capabilities, ultimately improving and
liberating productivity in hydrology work. It serves as a hydrological knowledge assistant,
providing domain-specific expertise and automated report generation services for water
resource professionals. It also powers AI applications for critical scenarios including river
evolution analysis, engineering computations, and hydrological testing with its visual analysis
capabilities. Furthermore, the system implements intelligent hydrological agents based on
the large model capable of flood prevention strategy formulation and emergency response
planning, streamlining end-to-end workflows.

A standout application is the riverbed terrain prediction module, which plays a crucial role in
flood control, channel maintenance, and water resource management. Traditional methods
often rely on survey vessels to perform intensive and inefficient riverbed depth measurements.
In this use case, the data from cross-sectional water depth measurements and high-frequency,
high-resolution river monitoring enabled by satellite remote sensing technology can be analyzed
by the large model and allows for highly efficient and accurate riverbed terrain predictions,
demonstrating significant operational and economic advantages over conventional methods.

Through this model, we have achieved 90% accuracy in hydrology data queries, approximately
30% reduction in report writing time, over 20% improvement in thalweg line drawing efficiency
and 80% accuracy in riverbed topography prediction.

Validated across diverse basin types, the system establishes a scalable paradigm for intelligent
water governance. Its architecture bridges AI capabilities with hydrological expertise, setting a
new standard for sustainable water management in the era of climate uncertainty.

Use Case Status: Operational

Partners: Changjiang Hydrological Bureau [3]

2�2 Benefits of use case

The introduction of the hydrology LLM drives digital and intelligent transformation in the water
management sector. By leveraging AI innovations, it significantly enhances hydrological work
efficiency and strengthens the intelligent management capabilities of water infrastructure.
The model represents a technological breakthrough in modernizing critical infrastructure,
promoting cutting-edge technological applications in water resource management.

By providing accurate hydrological data analysis and riverbed topography predictions, the model
plays a crucial role in forecasting and mitigating water-related risks induced by climate change. It
substantially improves flood prevention and water emergency management capacities, thereby

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