A web platform that collects weather data from social media and APIs, filters out fake reports using AI, and displays weather events on a dashboard for administrators.
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 real time posts, APIs, and citizen reports using specified hashtags like IMD.
AI Verification Engine
Identifies fake reports, removes duplicates, and verifies untrusted sources.
Event Categorizer
Automatically categorizes weather events such as rainfall, thunderstorms, and heatwaves.
Central Database
Stores collected information along with metadata like date, time, and GPS location.
Admin Dashboard
Provides real time visualization, analytics, and filtering by date, event, and location.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The evaluators will check if the platform successfully collects data using hashtags, uses machine learning to filter out fake reports, and provides an admin panel with date, event, and location filters.
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.
Specific APIs or social media platforms to integrate with?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Required performance benchmarks for real time processing scale?
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 SIH26069 itself — Collects real time posts, APIs, and citizen reports using specified hashtags like IMD. The brief asks for it by name.
Each one is quoted from a gap in the brief, not a guess about your team.
No measurable target
The description asks for a scalable platform and real time processing but provides no numerical targets for data volume, latency, or accuracy.
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 ai verification engine. How would you build that?
Decoded from SIH26069 itself — Identifies fake reports, removes duplicates, and verifies untrusted sources. The brief asks for it by name.
The brief asks for event categorizer. How would you build that?
Decoded from SIH26069 itself — Automatically categorizes weather events such as rainfall, thunderstorms, and heatwaves. The brief asks for it by name.
The brief asks for central database. How would you build that?
Decoded from SIH26069 itself — Stores collected information along with metadata like date, time, and GPS location. The brief asks for it by name.
The brief asks for admin dashboard. How would you build that?
Decoded from SIH26069 itself — Provides real time visualization, analytics, and filtering by date, event, and location. 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 evaluators will check if the platform successfully collects data using hashtags, uses machine learning to filter out fake reports, and provides an admin panel with date, event, and location filters.
This is the evaluator read for your problem statement, decoded from the brief's own wording.