Personalized heat stress early warning system for an urban area

Extreme heat events have led to significant human mortality and morbidity across all regions of the world, with the risk of heat stress among residents steadily increasing. In response, there is a growing need for early warning systems to help individuals plan their activities to mitigate these risks. This study aims to develop a Heat Stress Risk Index Forecast (HSRIF) system, currently being prototyped for the municipal boundary of Delhi (India). HSRIF is a novel Universal Thermal Climate Index (UTCI)-based personalised early warning system, developed to provide customized heat stress risk value to the user for the upcoming 5 days. The system leverages a novel approach by utilizing meteorological data generated through an open source numerical weather prediction model 'Weather Research and Forecasting model's Urban Canopy Model (WRF-UCM)' to calculate the UTCI at a high resolution of 333 m over the study area. The calculated UTCI is then categorically normalized to derive a hazard index. This hazard index is layered over a personalized vulnerability and exposure index tailored to individual users. The vulnerability and exposure indices are designed based on personal user information, categorized into three levels of severity derived from the literature. These parameters are further refined through expert opinions from both medical and non-medical fields, incorporating their insights using the Bayesian Best-Worst Method (BBWM). By amalgamating the hazard, vulnerability, and exposure indices, the system forms the UTCI-based HSRIF. This forecast is presented to users via an interactive dashboard, providing a detailed assessment of their heat stress risk over the upcoming five days.