Early Warning System for Glacial Lake Outburst Using Multi Source Data

Glacial Lake Outburst Floods (GLOFs) pose serious risks to ecosystems, infrastructure, and communities downstream. By combining several data sources, such as satellite imaging, remote sensing, hydrometeorological data, and real-time sensor networks, this study presents a unique early warning system (EWS) intended to mitigate these hazards. The EWS can monitor and forecast GLOF incidents more accurately and quickly by using machine learning algorithms and predictive models, which will improve preparedness and mitigation activities in areas that are at risk. The system continuously monitors glacial lakes using satellite imaging and remote sensing, gathering vital information on the dynamics of the lakes and the surrounding ecosystem. This data is paired with hydrometeorological data gathered from real-time sensor networks and ground-based stations, including temperature variations and precipitation levels.