The North Eastern Region (NER) frequently faces landslides, flash floods, road blockages, and slope failures due to heavy rainfall, fragile terrain, and unplanned hill cutting. These incidents often disrupt connectivity, damage infrastructure, delay emergency response, and isolate remote villages for days. Currently, monitoring of vulnerable zones is mostly reactive and dependent on manual reporting. There is limited use of real-time predictive systems for identifying high-risk zones and issuing
The goal is the to build an Al-powered early warning and monitoring system that can predicting and tracking landslide-prone areas in real-time across the North Eastern Region.
These incidents often disrupt connectivity, damage infrastructure, delay emergency response, and isolate remote villages for days Currently, monitoring of vulnerable zones is mostly reactive and dependent on manual reporting There is limited use of real-time predictive systems for identifying high-risk zones and issuing
The goal is the to build an Al-powered early warning and monitoring system that can predicting and tracking landslide-prone areas in real-time across the North Eastern Region. The expected system must: a. Collect and analyse data from: Rainfall patterns Soil moisture sensors Satellite imagery Terrain/slope data Historical landslide records b. Use AI/ML models to detect high-risk zones and predict possible landslide events. c. Provide real-time alerts to district administrations, disaster management authorities, and local communities. d. Integrate GIS mapping for visualization of vulnerable roads, villages, and infrastructure. e. Allow citizens/field officials to upload geo-tagged photos/videos of cracks, slope movement or blocked roads. f. Generate dashboards sho