Page 215 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

helping communities to both mitigate and adapt to the evolving environmental challenges.
The advanced predictive capabilities enable proactive responses to increasingly complex
climate-induced water system dynamics.

The model offers intelligent support for hydrological work, significantly enhancing disaster Change 4.2-Climate
prevention and mitigation capabilities in urban and rural communities. Particularly in flood
prevention, river management, and risk assessment, it effectively reduces water-related disaster
risks and strengthens community resilience. By providing precise, real-time insights, the model
empowers local authorities to make data-driven decisions that protect lives, infrastructure, and
economic assets.

2�3 Future Work

In the forthcoming phase, we aim to create a more comprehensive knowledge base by
integrating diverse data sources and advanced technologies. This will involve synthesizing
meteorological data, Geographic Information Systems (GIS), hydrological historical datasets,
and industry-specific historical records into a unified knowledge framework.

The strategic technological approach focuses on constructing an intelligent large model to
optimize and support water management mechanism models. We will leverage cutting-edge
technologies, including advanced sensing technologies, comprehensive network coverage,
large language model capabilities, and cloud rendering technologies.

Our primary objectives encompass enhancing digital twin foundational capabilities and
developing robust three-tier defense systems. This involves continuous exploration and
implementation in critical scenarios such as video cascade control, data aggregation, rainfall
radar infrastructure development, and communication guarantee in complex operational
environments.

Ultimately, the goal is to provide comprehensive, multi-dimensional support for water resource
management, driving technological innovation and operational efficiency in the water
management sector. By integrating advanced computational techniques with deep domain
expertise, we aim to transform how water resources are monitored, analyzed, and managed.

3 Use Case Requirements

REQ-01: It is required that the hydrology station collects data like water level and flow data,
rainfall and water quality data, soil and sediment data via different types of sensors and send
to the intelligent hydrology platform as needed.

REQ-02: It is required that the intelligent hydrology platform support data storage for further
fine-tuning of the LLM.

REQ-03: It is required to have multi-modal data processing for the LLM, including satellite
remote sensing data, field survey inputs, and historical hydrological records.

REQ-04: It is required to have cross-modal alignment for riverbed prediction, e.g., water depth
measurements and satellite imagery.
REQ-05: It is required to have 80%+ accuracy threshold for riverbed topography predictions.

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