An AI system that uses satellite, radar, and weather model data to predict heavy rainfall and flooding, for use by meteorologists and disaster management authorities.
Ministry of Earth Sciences (MoES) · India Meteorological Department · Software
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Decomposed from what the description asks for. Nothing added.
Data Ingestion Pipeline
Collects inputs from satellite, radar, observational weather, and numerical prediction models.
AI/ML Prediction Model
Processes the ingested data to forecast heavy rainfall and inundation risks.
Early Warning Dashboard
Displays alerts and prediction outputs for users to monitor weather threats.
Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.
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 data ingestion pipeline. How would you build that?
Decoded from SIH26071 itself: Collects inputs from satellite, radar, observational weather, and numerical prediction models. The brief asks for it by name.
The brief asks for ai/ml prediction model. How would you build that?
Decoded from SIH26071 itself: Processes the ingested data to forecast heavy rainfall and inundation risks. The brief asks for it by name.
The brief asks for early warning dashboard. How would you build that?
Decoded from SIH26071 itself: Displays alerts and prediction outputs for users to monitor weather threats. 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.
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.
The official description is only 175 characters, so we have held back the evaluator read and the risk flags rather than guess at them.