Extreme Heatwave Early Warning and Human Thermal Stress Index
Ministry of Earth Sciences (MoES) · Disaster Management · Software
The physiology is right, the computation is free, and the same-temperature-different-risk demo makes the argument for you — just present the health half as relative vulnerability grading rather than predicted mortality, because that data does not exist and the claim would not survive scrutiny.
What it actually is
Heat warnings in India are issued on air temperature alone, but the same temperature is survivable in dry air and lethal in humid air, because what kills people is the body's inability to shed heat. The ask is a warning system that computes what the weather will actually do to a human body rather than what the thermometer will read, mapped down to ward level so a city can act on it.
What to build
A heat risk platform computing an established physiological heat stress index from the four variables the statement names — temperature, humidity, wind and radiation — rather than dry-bulb temperature alone, forecast three to five days ahead and rendered at ward level over a city, combined with a vulnerability layer weighting each ward by the exposure factors named including elderly and outdoor-worker density so the same thermal stress maps to different risk in different places, a colour-coded GIS dashboard with graded alerts, automated public health advisories tied to each grade, and an API triggering the specific municipal actions the statement lists such as opening cooling centres and shifting outdoor work hours.
Smallest thing that wins the room
Show two wards forecast at the same air temperature receiving different alert grades because one is humid and low-wind, then display the thermal stress curve for both and the advisory that fires for the dangerous one.
How crowded this one gets
A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.
Quieter than 40% of the 226 · #135 of 226 by expected field
A normal-sized field. Your idea has to be good, not miraculous.
Why: central ministry statements sat below the average.
This is a guess, not a fact
Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.
The scores
The number is the shorthand. The line under it is the reason.
Acceptance potential
4/5The gap this addresses is real — warnings genuinely are issued on dry-bulb temperature and the physiology genuinely is ignored — the computation is free and well founded, the demo makes its own case visually, and heat is a rising hazard that far fewer teams choose than flooding or cyclones.
Feasibility
3/5The thermal stress half is entirely achievable — the established indices have published formulations and every input variable comes free from standard forecast fields — but the mortality risk index the statement also demands needs historical heat-attributable death records at local granularity, and India does not publish those, so half the ask has no data behind it.
Innovation scope
3/5The candidate indices are named, the lead time, the granularity and the delivery channels are specified, but how you combine thermal stress with demographic vulnerability into a single actionable grade — which is the actual product decision — is left entirely open.
Clarity
4/5The physiological argument is made precisely and correctly with a worked contrast between the same temperature at different humidities, the candidate indices are named, and the lead time, granularity, delivery channels and municipal triggers are all specified.
Effort
HeavyIndex computation is straightforward but assembling ward-level demographic vulnerability layers, downscaling forecast fields to ward granularity, building the GIS dashboard and wiring the advisory and alerting paths is four workstreams with the vulnerability data the awkward one.
Demo-ability
EasyTwo wards at the same temperature receiving different alert grades makes the entire argument of the statement visible in a single frame, and it is the rare demo that teaches the judge why the product needs to exist while demonstrating that it works.
In its favour
- Green flag: The core computation is free, well founded and standardised — the heat stress indices have published formulations and every input arrives in ordinary forecast output, so there is no data or modelling risk in the half that matters
- Green flag: The two-wards-same-temperature demo teaches the judge why the statement exists and proves the system works in the same thirty seconds, which is a rare property
- Green flag: Established heat action plans in Indian cities give you real, documented intervention thresholds to map your alert grades onto, so the advisories can be grounded rather than invented
- Green flag: Heat is genuinely under-attempted relative to flood and cyclone statements despite being a larger and growing killer, so the field here will be thinner than the topic deserves
Against it
- Red flag: The statement also demands a mortality risk index and heat-attributable death data is not published at local granularity in India, so that half rests on either published epidemiological relationships applied from elsewhere or nothing at all — say which you did
- Red flag: Wet-bulb globe temperature needs radiation and mean radiant temperature, which are frequently approximated badly; use a documented approximation and cite it rather than inventing a shortcut
- Red flag: Ward-level output implies a spatial resolution that coarse forecast fields do not have, so be explicit that you are attributing a coarser field to wards via vulnerability weighting rather than claiming ward-resolution meteorology
- Red flag: Predicting mortality is a claim with real consequences and weak evidence at this scale — presenting a relative risk grading is defensible, presenting projected death counts is not
What you will be writing
- WBGT and UTCI thermal stress computation
- forecast field ingestion for temperature, humidity, wind and radiation
- census-derived ward vulnerability weighting
- graded alert thresholds mapped to heat action plan triggers
- PostGIS ward-level risk mapping
- SMS and messaging advisory API
- Biometeorology and heat health
- Public health early warning
- Urban climate risk
Prior art to read before you start
physiological heat stress indexing · vulnerability-weighted hazard mapping · impact-based warning and advisory triggering
Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.