Fews: IoT-Based Flood Early Warning System for Barangay Doña Imelda, Quezon City

Flooding remains one of the most destructive natural hazards affecting urban communities in the Philippines, particularly those located near major river systems. This study presents the design, development, and evaluation of an Internet of Things (IoT)-based Flood Early Warning System (FEWS) for Barangay Doña Imelda, Quezon City, an area highly vulnerable to recurring floods. The system utilizes an ESP32 microcontroller integrated with an HC-SR04 ultrasonic sensor for water-level measurement, a Neo-6M GPS module for geolocation, and a Ra-01 LoRa module for long-range data transmission. Real-time water-level data are transmitted to a cloud-based database and visualized through a web dashboard and an Android mobile application, enabling timely dissemination of flood alerts to residents and local authorities. A simple linear regression model is incorporated to forecast short-term water-level trends, enhancing preparedness and response capability. The system is powered by solar energy to ensure continuous operation during power interruptions. Development followed the Rapid Application Development (RAD) methodology to support iterative prototyping and user-centered design. System evaluation using the Technology Acceptance Model (TAM) yielded an overall weighted mean of 4.52, indicating high user acceptance in terms of perceived usefulness, ease of use, and behavioral intention to use. Compliance assessment based on ISO 22328-1 resulted in an overall weighted mean of 4.45, demonstrating strong conformity with international standards for community-based early warning systems. The results confirm that the proposed FEWS effectively enhances flood preparedness, supports informed decision-making, and contributes to community resilience.

Internet of Things, Flood Early Warning System, Linear Regression, Rapid Application Development

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