SIH26001SoftwareDisaster Management

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

Ministry of Development of North Eastern Region (MDoNER)

Ideas submitted: 500 / 500
Deadline: 30 September 2026
Category: Software
Theme: Disaster Management

Looks like it needs

AI / MLIoT / Embedded

Background

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 early warnings to authorities and local communities. With increasing climate vulnerability in the region, there is a need for an AI-enabled real-time monitoring and prediction system that can help authorities take preventive action before disasters occur.

Description

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.

Expected Solution

A scalable Al-based software platform with

• Real-time GIS dashboard and risk heatmaps • AI/ML-based predictive analytics engine • Mobile/web application for field reporting and alerts. • Integration with IMD weather APIs, satellite feeds, and sensor data • Automated SMS/app-based early warning system • Cloud-based architecture with offline sync support for remote regions The solution should improve disaster preparedness, reduce loss of life and infrastructure damage, and strengthen climate-resilient governance in the North Eastern Region.

Official listing Match my team

How contested this one is

as of 28 Sept

500ideas submitted

That puts it at the top of the 240 statements that have any ideas at all, out of 240 on the board. It has not moved since the last check.

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Counted from the official portal twice a day. The portal itself only shows today.

How a winner reads this one

Fourteen ideas submitted, the busiest on the board

This is the most contested statement on the board, so the question is not whether you can build it. It is what you do that the other teams will not.

Start by not overclaiming. You cannot predict a landslide. Nobody can predict a specific slope failing at a specific hour, and a juror who works in disaster management knows that better than you do. What you can do is combine rainfall thresholds, soil moisture and slope data into a risk level for a zone. Say risk rose sharply, not a landslide will occur. Teams that promise prediction get taken apart on that one word.

The real gap is after the warning. Almost everyone will stop at a dashboard, and the statement is about remote villages being cut off and emergency response arriving late. So answer the last mile. Who gets told, on what device, in which language, when the network is already down.

And have an answer for false alarms. Evacuate a village wrongly twice and nobody moves the third time. That is the failure mode that kills these systems in the field, and nobody will mention it.

Zaid Sayyed · SIH 2025 national winner

What a jury will ask about this

01“Who actually faces this problem today?”
What works: Naming one real person and what they do instead right now. Reading the statement back is not an answer, they already read it.
02“This already exists. Why yours?”
What works: That existing tools are consumer products. Yours is built for the ministry, works offline, in the local language, on official data.
03“Then why has nobody solved it yet?”
What works: The real blocker. No connectivity, no incentive, nobody owns the data. You only know this if you read the ministry's own reports.

All 18 questions, with the trap answers →

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