HeatWatch Rizal is an intelligent heat risk monitoring and early warning system powered by Machine Learning and Geographic Information Systems (GIS), designed to protect communities across Rizal Province from extreme heat dangers.
Real-Time Monitoring
HeatWatch continuously tracks temperature and humidity data from all 13 municipalities and 1 component city of Rizal Province, plus 3 nearby Metro Manila reference points, using the Open-Meteo weather API. The interactive GIS map visualizes heat risks in real-time, helping you identify danger zones instantly.
AI-Powered Temperature Forecasting
The system uses three trained machine learning models — Random Forest (95.6% R²), Decision Tree (93.3% R²), and K-Nearest Neighbors (91.9% R²) — to forecast temperature 6 hours ahead. These predictions are then classified into heat risk categories: Normal, Moderate, High, and Dangerous. Switch between models and compare their performance on the ML Models page.
Flexible Alert System
Send heat alerts your way — manually trigger instant email notifications with 6-hour ML forecasts to any recipient, or schedule automatic recurring alerts at your preferred time intervals. Select locations, set your time gap, and let HeatWatch handle the rest.
Data-Driven Insights
All weather readings, risk classifications, ML predictions, and alert logs are stored in a structured database. This continuously growing dataset of real historical weather data serves as a foundation for future model retraining, climate research, and policy-making.
Why It Matters
The Philippines faces increasing heat-related health risks due to climate change. HeatWatch empowers local governments, emergency responders, and communities with actionable data to make informed decisions, issue timely warnings, and ultimately save lives.
Built by Engr. Brian Ezekiel D. Batalon, ECE, ECT, SO2