A software tool that takes block-level weather forecasts and downscales them to panchayat-level data for farmers and agriculture advisors.
Ministry of Earth Sciences (MoES) · India Meteorological Department · Software
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Decomposed from what the description asks for. Nothing added.
Data Ingestion Module
Takes low-resolution block-level weather data and forecasts as input.
Downscaling Engine
Infers high-resolution panchayat-level plots, data, and information from the low-resolution inputs.
Advisory Generation Service
Produces agro-meteorological advisories based on the high-resolution weather data.
User Interface
Displays the downscaled weather information and advisories to end users.
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 statistical or machine learning methods are expected for downscaling?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What format the input and output data must be in?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What specific variables need to be forecast?
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.
Each one is quoted from a gap in the brief, not a guess about your team.
Never says what to deliver
The text only states the high-level goal of downscaling weather forecasts without defining specific system workflows or deliverables.
No measurable target
There are no accuracy percentages or performance metrics specified for the downscaling process.
No specific user named
The description mentions agro-meteorological advisory services generally without specifying the exact user role or interface requirements.
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 data ingestion module. How would you build that?
Decoded from SIH26074 itself — Takes low-resolution block-level weather data and forecasts as input. The brief asks for it by name.
The brief asks for downscaling engine. How would you build that?
Decoded from SIH26074 itself — Infers high-resolution panchayat-level plots, data, and information from the low-resolution inputs. The brief asks for it by name.
The brief asks for advisory generation service. How would you build that?
Decoded from SIH26074 itself — Produces agro-meteorological advisories based on the high-resolution weather data. The brief asks for it by name.
The brief asks for user interface. How would you build that?
Decoded from SIH26074 itself — Displays the downscaled weather information and advisories to end users. 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.