Development of an IoT-Based System for Early Detection of Flooding and QGIS Mapping of Affected Areas in Batangas State University-Pablo Borbon Campus

Abstract

This study investigates the effectiveness of an IoT-based Early Warning Flood Alert System developed to improve disaster preparedness and community safety in flood-prone areas. The system integrates water-level detecting modules, GPS technology, and a QGIS-based mapping platform to accurately locate, monitor, and display areas experiencing rising water levels. The prototype underwent comprehensive testing, including evaluations of sensing accuracy, system response time, real-time data transmission, and durability under varying environmental conditions. Results indicate that the system performs reliably, providing consistent and precise updates as water levels change. Its ability to issue early alerts allows users and local authorities to take immediate precautionary measures, reducing the risk of property damage and ensuring community readiness during potential flooding events. Overall, the study concludes that the IoT-based system offers substantial benefits, demonstrating strong potential as a low-cost, scalable, and efficient solution for flood monitoring. Its implementation contributes positively to the surrounding community and supports Batangas State University–Pablo Borbon Campus in strengthening preparedness, resilience, and risk-reduction initiatives.

Introduction

In 2025, the Philippines experienced multiple typhoons, including a supertyphoon that caused significant casualties and damage to infrastructure. Flooding remains one of the country’s most frequent and destructive natural hazards due to its location within the Pacific typhoon belt, inadequate drainage systems, and intensifying extreme weather events linked to climate change. These floods disrupt daily life, including academic institutions like Batangas State University – Pablo Borbon Campus, where rising water levels frequently interrupt operations. Existing campus emergency preparedness is limited, with slow manual warning systems and insufficient evacuation plans, resources, and counseling support.

To address this, the study proposes a Smart Flood Alert System integrating Internet of Things (IoT) water-level sensors with QGIS mapping. This system aims to provide real-time monitoring, precise spatial visualization of flood-prone areas, and automatic alerts, enhancing campus preparedness, safety, and disaster-risk-reduction efforts.

Objectives:

1. Implement IoT-based water-level and environmental sensors to monitor flood conditions.

2. Integrate QGIS for geospatial mapping of monitored data and flood-affected areas.

3. Develop an automatic alarm system triggered by critical water levels.

4. Evaluate system performance for accuracy, responsiveness, reliability, and usability.

5. Assess overall effectiveness in supporting campus flood preparedness.

Materials and Methods:

• Design: Experimental-developmental approach focused on prototype creation and evaluation.

• Components: Water-level sensors, ESP32/Arduino microcontroller, Wi-Fi/LoRa module, rechargeable power, web dashboard, and QGIS software.

• Development: Enclosure fabrication, sensor calibration, microcontroller programming, dashboard setup, and GIS layer preparation.

• Testing: Accuracy tests, response time measurement, communication stability, flood simulation, and QGIS mapping validation.

Results:

• Functionality: The system operated efficiently across all components. Sensors accurately detected water levels, data processing was fast, communication was stable, QGIS mapping reflected flood-prone areas in real-time, and automatic alerts were successfully sent.

• Safety: Full compliance with safety protocols; sensors, wiring, power supply, enclosure, and modules were handled securely and protected from hazards.

• Feasibility: Initial assessments indicate the system is practical, cost-effective, sustainable, and suitable for long-term use.

Conclusion

In connection with the finding of the study, the following conclusion were drawn: 1) In connection with the findings of the study, the following conclusions were drawn:The system successfully detects potential floods by analyzing sensor data against predefined thresholds, ensuring timely and accurate alerts. 2) The integration of multiple sensors—such as ultrasonic, motion, and light sensors—provides layered detection and strengthens the system’s overall reliability. 3) The automatic activation of LEDs, signal lights, relay controls, and SMS alerts demonstrates the system’s capability to respond dynamically during flood risks. 4) Incorporating machine learning in future developments could enhance predictive capabilities, enabling more proactive and early warning alerts. 5) Expanding communication systems and implementing backup power sources will further improve the system’s functionality and reliability during emergencies, making it a more comprehensive flood management solution.

References

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Copyright

Copyright © 2025 John Aeron P. Capila, Lance Nathanyl P. Caraig, Lance Rayver N. Castor, Juan Miguel P. Clemeno, Dhoret Margaret P. De Guzman, Myka Laurence M. De Lima, Angel Darlene C. Despues . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.