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SIH 2026

Smart India Hackathon · sih.gov.in

226 statements
SoftwareDisaster ManagementSIH26083

Extreme Heatwave Early Warning and Human Thermal Stress Index

Ministry of Earth Sciences (MoES)

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Organisation
Ministry of Earth Sciences (MoES)
Department
National Centre for Medium Range Weather Forecasting (NCMRWF)
Category
Software
Theme
Disaster Management
Submission deadline
20 September 20262026-09-20
Ideas submitted
0/500
Serial number
83
Data captured on
2026-08-23
Dataset link
No dataset link published
Contact info
No contact published
Youtube link
No video published
Problem brief

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).

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