A web platform and mobile app that predicts landslides using AI and weather data, alerts authorities and communities, and lets field workers upload slope damage reports offline.
Ministry of Development of North Eastern Region (MDoNER) · Software
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Decomposed from what the description asks for. Nothing added.
Predictive AI Engine
Analyzes weather feeds, terrain data, satellite imagery, and historical records to forecast landslide risk levels.
GIS Risk Dashboard
Displays risk heatmaps, vulnerable roads, village locations, and emergency response priorities on interactive maps.
Offline Field Reporting App
Allows citizens and field officers to capture geo-tagged photos or videos of ground cracks and road blockages offline and sync later.
Automated Early Warning System
Sends real-time multilingual SMS and app notifications to district authorities and local community members.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
Evaluators will test low-network functionality and offline sync for remote areas, along with multilingual alerts for local communities. They will also verify GIS visualization of risk zones and integration with weather data feeds.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
Required prediction accuracy level or target lead time?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific list of regional languages to support?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Source or format for real-time soil moisture sensor data?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Access details for historical landslide records in the region?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Each one is quoted from a gap in the brief, not a guess about your team.
No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.
No measurable target
The text requires an AI model to predict landslides but does not define measurable performance targets such as accuracy percentage or warning time.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for predictive ai engine. How would you build that?
Decoded from SIH26001 itself — Analyzes weather feeds, terrain data, satellite imagery, and historical records to forecast landslide risk levels. The brief asks for it by name.
The brief asks for gis risk dashboard. How would you build that?
Decoded from SIH26001 itself — Displays risk heatmaps, vulnerable roads, village locations, and emergency response priorities on interactive maps. The brief asks for it by name.
The brief asks for offline field reporting app. How would you build that?
Decoded from SIH26001 itself — Allows citizens and field officers to capture geo-tagged photos or videos of ground cracks and road blockages offline and sync later. The brief asks for it by name.
The brief asks for automated early warning system. How would you build that?
Decoded from SIH26001 itself — Sends real-time multilingual SMS and app notifications to district authorities and local community members. The brief asks for it by name.
Where does your data come from — a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: evaluators will test low-network functionality and offline sync for remote areas, along with multilingual alerts for local communities.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will also verify GIS visualization of risk zones and integration with weather data feeds.
This is the evaluator read for your problem statement, decoded from the brief's own wording.