SIH 2026 Problem Statements
Master Explorer & Dataset

AI-Based early warning and landslide Risk Monitoring System in NER

🏛️ Ministry of Development of North Eastern Region (MDoNER)· Ministry of Development of North Eastern Region (MDoNER)

This problem statement proposes the development of an Al-powered early warning and monitoring platform capable of predicting and tracking landslide-prone areas in real time across the North Eastern Region. The solution should: 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 identify 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 showing: • Risk severity levels • Road connectivity status • Weather-linked risk forecasts • Emergency response prioritisation. Support multilingual notifications and low-network/offline functionality for remote areas.