A web dashboard and AI backend that uses satellite data to predict localized cloudbursts, thunderstorms, and flash floods two to six hours in advance for disaster management authorities.
Ministry of Earth Sciences (MoES) · National Centre for Medium Range Weather Forecasting (NCMRWF) · Software
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Decomposed from what the description asks for. Nothing added.
Data Ingestion Pipeline
A module that ingests and aligns satellite observations, reanalysis data, and digital elevation models onto a unified spatiotemporal grid.
Multi-Task Inference Engine
An AI model using a shared neural network backbone to simultaneously predict thunderstorms, cloudbursts, and flash floods.
Spatial Risk Dashboard
An interactive web dashboard for disaster management authorities displaying dynamic probability risk maps overlaid on a digital elevation model.
Automated Alerting API
An API that pushes automated, categorized alerts and spatial risk data to first responders when critical meteorological thresholds are breached.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check if the AI model can process multi-modal satellite and reanalysis data to output simultaneous predictions within a two to six-hour lead time, while displaying results on an interactive dashboard with elevation overlays.
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.
Exact geographical region or test coordinates for the pilot warning system?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific alert delivery channels, such as SMS gateway or mobile push notifications, required for first responders?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
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 SIH26077 itself — A module that ingests and aligns satellite observations, reanalysis data, and digital elevation models onto a unified spatiotemporal grid. The brief asks for it by name.
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.
The brief asks for multi-task inference engine. How would you build that?
Decoded from SIH26077 itself — An AI model using a shared neural network backbone to simultaneously predict thunderstorms, cloudbursts, and flash floods. The brief asks for it by name.
The brief asks for spatial risk dashboard. How would you build that?
Decoded from SIH26077 itself — An interactive web dashboard for disaster management authorities displaying dynamic probability risk maps overlaid on a digital elevation model. The brief asks for it by name.
The brief asks for automated alerting api. How would you build that?
Decoded from SIH26077 itself — An API that pushes automated, categorized alerts and spatial risk data to first responders when critical meteorological thresholds are breached. 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: the jury will check if the AI model can process multi-modal satellite and reanalysis data to output simultaneous predictions within a two to six-hour lead time, while displaying results on an interactive dashboard with elevation overlays.
This is the evaluator read for your problem statement, decoded from the brief's own wording.