Algorithmic Integration of the Rothfusz Regression Model for Real-Time Heat Index Early Warning Systems
Year: 2026
Type: article
Abstract: The escalating frequency of extreme temperature anomalies necessitates robust, localized adaptation strategies. This study presents the development and deployment of Uligmat.ph, a localized, real-time heat index monitoring and automated early warning system (EWS) for the Province of Antique, Philippines. Addressing Sustainable Development Goals 13, 3, 9, and 11, the system bridges critical infrastructure gaps by transitioning from static synoptic stations to a dynamic, client-server architecture utilizing the OpenWeather One Call API 3.0. The methodology employs a System Development Life Cycle (SDLC) framework, integrating the Rothfusz Regression Equation and DOST-PAGASA classification algorithms to categorize environmental data into actionable risk levels. A pivotal enhancement involves transitioning the architecture to a server-side PHP backend, facilitating automated cron-based weather polling, threshold analysis, and SMS gateway orchestration. Results demonstrate a highly secure infrastructure fortified with AES-256-GCM encryption, PostgreSQL Row-Level Security (RLS), and comprehensive rate limiting mechanisms to ensure data integrity and mitigate alert fatigue. The deployed platform empowers the Provincial Disaster Risk Reduction and Management Office (PDRRMO) and local municipalities to issue precise, timely health advisories, thereby mitigating heat-related morbidities such as hypertension and heatstroke. Ultimately, this cost-effective, scalable model enhances agricultural resilience and disaster preparedness in vulnerable, high-risk communities.
Author: Mission, Roger
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7219987439