Hilly states in India are highly vulnerable to landslides and flash floods,which often occur with very short warning times. These sudden events result in significant loss of lives and property, and current early warning mechanisms are inadequate for hyper-local prediction and timely evacuation. •
The proposed initiative aims to develop a predictive system that integrates multiple data sources - rainfall data, soil moisture sensors,slope stability models, historical landslide inventories, and real-time IoT inputs.
Hilly states in India are highly vulnerable to landslides and flash floods,which often occur with very short warning times These sudden events result in significant loss of lives and property, and current early warning mechanisms are inadequate for hyper-local prediction and timely evacuation. •
The proposed initiative aims to develop a predictive system that integrates multiple data sources - rainfall data, soil moisture sensors,slope stability models, historical landslide inventories, and real-time IoT inputs. By combining these datasets, the system will generate hyper-local forecasts at the village or ward level, providing sufficient lead time for evacuation and risk mitigation. •