Extreme Heatwave Early Warning and Human Thermal Stress Index
“Traditional weather forecasts only look at air temperature to warn people about heatwaves, ignoring humidity, wind, and sunlight which actually kill the human body. This project builds an intelligent early warning system that calculates true physiological heat stress and mortality risk 3 to 5 days in advance. It gives municipal authorities a ward-level GIS dashboard and triggers automated emergency alerts to save lives.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An intelligent, end-to-end impact-based early forecasting and decision support software platform that computes human thermal stress indices, predicts heat-induced mortality spikes, and automates multi-channel public health advisories.
WHO IS IT FOR?
Problem: National Centre for Medium Range Weather Forecasting (NCMRWF) and Ministry of Earth Sciences. Users: Municipal disaster management authorities, city planners, healthcare infrastructure managers, and vulnerable citizens via automated regional advisories.
WHY DOES IT EXIST?
Current public health infrastructure relies solely on dry-bulb temperature thresholds, failing to account for compounding environmental factors like humidity and solar radiation, leading to unpredicted mortality spikes among vulnerable populations like outdoor workers and the elderly.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Gridded meteorological weather forecasts (temperature, relative humidity, wind speed, solar radiation), historical public health/hospitalization records, ward-level demographic data (elderly density, outdoor worker hotspots), and GIS boundary files.
Compute advanced thermal indices (WBGT, UTCI, Heat Index), feed environmental and demographic features into a predictive regression/classification pipeline for 3-5 day mortality/hospitalization risk forecasting, and map outputs to geo-fenced administrative wards.
A dynamic GIS-mapped color-coded heat risk dashboard, automated SMS/WhatsApp alerts for administrators and citizens, and actionable deployment triggers for cooling centers and work-hour restrictions.
CONSTRAINTS
- Must handle hyper-local spatial interpolation from coarse meteorological grid points to ward-level resolutions.
- Must operate efficiently under low-bandwidth network conditions for rural or semi-urban municipal offices.
- Multilingual alert generation supporting regional Indian languages for mass citizen reach.
- Real-time processing capability for 3-to-5-day rolling forecast models.
JURY METRICS
- Prediction accuracy (RMSE/MAE for thermal index calculations and mortality risk classification F1-score).
- Inference latency of the forecasting and GIS dashboard rendering pipeline.
- False alarm rate for automated emergency trigger notifications.
- UI/UX simplicity and clarity for non-technical disaster management officials.
CORE BOTTLENECK
Accurately correlating multi-variable thermal stress indices (like UTCI/WBGT) with historical health outcomes in the absence of granular, localized daily mortality datasets across Indian cities.
Why You Should Choose This PS (Pros)
- Extremely high social impact aligned with national disaster management priorities.
- Clear, structured deliverables ranging from algorithmic calculation to GIS mapping and API alerting.
- Strong 'wow factor' through predictive visualization and automated proactive municipal intervention triggers.
Challenges & Risks to Watch (Cons)
- Granular public health and mortality data required for training predictive models is often sparse or unstructured in India.
- Complexity in validating advanced meteorological indices like UTCI without proprietary weather station sensor networks.
- Crowded domain if teams rely purely on basic temperature-based dashboards without true physiological modeling.
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
In recent years, the frequency, duration, and intensity of heatwaves across India have escalated sharply due to climate change. However, traditional meteorological warnings rely almost exclusively on ambient dry-bulb temperature thresholds. This creates a critical vulnerability:standard forecasts ignore the deadly compounding effects of relative humidity, wind speed, and solar radiation on the human body.
A temperature of 40°C at 20% humidity feels vastly different from 40°C at 70% humidity—the latter can be fatal. Current public health infrastructure lacks localized, impact-based forecasting that translates raw weather data into actual physiological risk, human thermal stress levels, and projected mortality rates.
The challenge is to design an intelligent, localized early warning system that shifts heatwave forecasting from 'what the weather will be' to 'what the weather will do' to human health.
Participants need to build a predictive platform that computes a comprehensive Human Thermal Stress Index (integrating temperature, humidity, wind, and radiation) and links it directly to an automated Mortality Risk Index. The system should offer high-resolution forecasts to help municipal corporations, healthcare systems, and disaster management authorities deploy targeted,preemptive interventions.
Develop algorithms to calculate advanced heat stress metrics such as the Wet-Bulb Globe Temperature (WBGT), Universal Thermal Climate Index (UTCI), or Heat Index (HI) rather than relying on temperature alone. Integrate historical public health, demographic (e.g., elderly or outdoor worker density), and localized weather data to predict heat-induced mortality and hospitalization spikes 3 to 5 days in advance. A dynamic GIS-mapped dashboard providing colorcoded, hyper-local alerts (Zone/Ward level) paired with actionable, automated public health advisories. An API capable of pushing automated SMS/WhatsApp regional alerts or localized triggers for city administration to initiate heat action plans (e.g., opening cooling centers, adjusting power grids, shifting outdoor work hours).