An AI and ML software system that identifies, classifies, and predicts tropical cyclone patterns using satellite data for meteorologists.
Ministry of Earth Sciences (MoES) · India Meteorological Department · Software
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Decomposed from what the description asks for. Nothing added.
Satellite Data Ingestion
A module to load and process multi-source satellite data feeds.
Cyclone Identification Engine
An AI and ML model to detect tropical cyclone patterns from the data.
Pattern Classification Tool
A component to categorize the identified cyclones into different types.
Prediction Module
A predictive system to forecast the movement and behavior of cyclones.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
No specific demand we can quote, which means there is nothing concrete to prepare against.
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.
What specific satellite data sources must be used?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What accuracy targets the prediction model must achieve?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What output format the system should provide?
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 satellite data ingestion. How would you build that?
Decoded from SIH26070 itself: A module to load and process multi-source satellite data feeds. The brief asks for it by name.
The brief asks for cyclone identification engine. How would you build that?
Decoded from SIH26070 itself: An AI and ML model to detect tropical cyclone patterns from the data. The brief asks for it by name.
The brief asks for pattern classification tool. How would you build that?
Decoded from SIH26070 itself: A component to categorize the identified cyclones into different types. The brief asks for it by name.
The brief asks for prediction module. How would you build that?
Decoded from SIH26070 itself: A predictive system to forecast the movement and behavior of cyclones. 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.
Each one is quoted from a gap in the brief, not a guess about your team.
Never says what to deliver
The description only asks to develop a system without detailing specific features or deliverables.
No measurable target
There are no accuracy or performance targets mentioned for the identification and prediction.
No specific user named
The text does not name who will ultimately use the system.
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.