Integrating Machine Learning and Community-Based Approaches for Enhanced Early Warning Systems in Cascading Hazard Zones: A Case Study from Melamchi, Nepal

This study presents a comprehensive early warning system for cascading hazards in the Melamchi region of Nepal that integrates satellite data, machine learning, and community engagement. Ground-based surveys complement this by mapping risk zones, with local citizens actively marking flood entry points and landslide risk areas. Utilizing random forest regression, we assess flood and landslide susceptibility, enhancing the predictive accuracy of the model. This multifaceted approach not only improves hazard prediction but also fosters community resilience and preparedness, offering a robust model for disaster risk reduction in vulnerable regions. This approach strengthens the technical aspect and empowers local communities by engaging them in disaster management processes. By combining scientific data with local knowledge, long-term preparedness can be achieved in vulnerable regions. This model could be replicated in other areas exposed to similar risks.