Hybrid AI-Geo-informatics framework for river course change prediction and disaster risk mitigation

Area of Science:

• Geosciences
• Environmental Science
• Data Science

Background:

• Rivers are dynamic geomorphological systems prone to natural course alterations.
• Sudden river diversions, like the 2008 Kosi River event, cause severe flooding, displacement, and land loss.
• Conventional monitoring methods (manual satellite interpretation, hydrological modeling) are slow, lack resolution, and offer limited predictive power.

Purpose of the Study:

• To develop an AI-based Geo-Informatics framework for predicting river course changes.
• To enhance disaster risk mitigation strategies for riverine environments.
• To enable proactive river management and sustainable water resource utilization.

Main Methods:

• Utilized multi-source databases including satellite imagery, hydrology, rainfall, and soil data.
• Developed a hybrid AI architecture combining machine learning (Random Forest, Gradient Boosting) and deep learning (CNN, U-Net, LSTM/ConvLSTM).
• Modeled spatial river morphology and temporal pattern evolution for predictive analysis.

Main Results:

• The framework generates predictive geospatial risk maps and forecasts of river migration.
• Demonstrated the capability for interactive visualization products to aid disaster preparedness.
• Showcased the transformation of river monitoring from reactive to proactive prediction.

Conclusions:

• AI-driven Geo-Informatics offers a powerful tool for transforming river monitoring and management.
• The framework contributes to resilience building aligned with the UN Sendai Framework and SDGs.
• Proactive prediction enables better disaster preparedness and sustainable water resource management.