Floodsense North Caloocan: An IoT-Based Community Flood Monitoring and Reporting System
Authors
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Bachelor of Science in Computer Science, St. Clare College of Caloocan (Philippines)
Article Information
DOI: 10.51584/IJRIAS.2026.11060147
Subject Category: Computer Science
Volume/Issue: 11/6 | Page No: 1921-1944
Publication Timeline
Submitted: 2026-06-12
Accepted: 2026-06-17
Published: 2026-07-03
Abstract
Flooding remains one of the most recurring natural disasters in the Philippines, causing significant disruptions to communities, infrastructure, and public safety. North Caloocan is among the areas frequently affected by flooding, particularly during periods of heavy rainfall and typhoons. Traditional flood monitoring methods often rely on manual observation and delayed reporting, resulting in slow dissemination of information and reduced effectiveness of emergency response efforts. This study developed FloodSense North Caloocan, an Internet of Things (IoT)-based community flood monitoring and reporting system designed to provide real-time flood information and improve disaster preparedness within the community.
The system utilizes an ESP32 microcontroller and a waterproof ultrasonic sensor to monitor water levels and transmit data to a cloud-based database. A web application developed using the MERN stack (MongoDB, Express.js, React, and Node.js) enables real-time monitoring, community reporting, flood mapping, and notification services. A quantitative research approach was employed to assess community needs and system acceptance.
Results indicate that residents strongly support the implementation of a real-time flood monitoring system and recognize its potential to improve awareness, preparedness, and response during flood events. Prototype-level validation demonstrated consistent water-level estimation using the JSN-SR04T ultrasonic sensor. Functional testing showed web dashboard update times ranging from 1–3 seconds, notification delays ranging from 4–10 seconds with a 100% notification success rate, and reliable data transmission across all validation trials. These findings indicate that the system can provide timely and dependable flood information for community-based disaster preparedness. The findings demonstrate that FloodSense North Caloocan can serve as an effective tool for enhancing community resilience and supporting local disaster risk reduction and management initiatives.
Keywords
Flood Monitoring, Internet of Things, FloodSense, North Caloocan, Disaster Management, Real Time Monitoring, Community Reporting
Downloads
References
1. Ahmed, T., Rahman, M., & Islam, S. (2023). Wireless sensor and web-based decision support system for flood monitoring and environmental analysis. International Journal of Environmental Monitoring, 15(2), 101–115. [Google Scholar] [Crossref]
2. Arante, J. P., Santos, M. L., & Reyes, R. T. (2025). Secured IoT-based flood monitoring and forecasting system using a genetic-algorithm neuro-fuzzy network. Philippine Journal of Computing and Information Systems, 12(1), 45–60. [Google Scholar] [Crossref]
3. Bentoso, J. R., Mendoza, L. P., & Torres, C. D. (2021). Web-based flood warning decision support system for disaster management in Leyte. Journal of Philippine Information Technology Research, 8(3), 33–48. [Google Scholar] [Crossref]
4. Byaruhanga, J., Nsubuga, F., & Mugume, I. (2024). Evolution of flood prediction and forecasting models for flood early warning systems: A scoping review. Environmental Monitoring and Assessment, 196(4), 1–21. [Google Scholar] [Crossref]
5. Fernández-Nóvoa, D., García-Feal, O., & Gómez-Gesteira, M. (2024). Enhancing flood risk management: A comprehensive review on flood early warning systems with emphasis on numerical modeling. Water, 16(2), 1–25. [Google Scholar] [Crossref]
6. Gersaniva, P. R., & Damasco, A. J. (2025). Innovative flood monitoring system for community disaster preparedness and response. Asian Journal of Disaster Management, 14(1), 70–85. [Google Scholar] [Crossref]
7. Marzukh, S., Abdullah, N., & Hassan, M. (2018). Flood detection and warning system (FloWs) using Arduino Uno and GSM technology. International Journal of Advanced Engineering Research, 9(4), 55–67. [Google Scholar] [Crossref]
8. Montesa, K. A., Villanueva, R. M., & Cruz, J. L. (2023). Development of a coastal flood monitoring device using ultrasonic sensors and LoRa technology. Philippine Engineering and Technology Journal, 18(2), 91–108. [Google Scholar] [Crossref]
9. Sanz, A., Rodriguez, P., & Moreno, J. (2024). Cloud-based monitoring of hydrological processes using dense sensor networks and NB-IoT connectivity. Sensors, 24(5), 1–19. [Google Scholar] [Crossref]
10. Seva, R. R., Bautista, A. P., & Ramos, D. C. (2025). FloodCast: A user-centered flood monitoring system for improving community engagement and preparedness. Journal of Community Informatics and Disaster Resilience, 11(2), 50–68. [Google Scholar] [Crossref]
11. Tolentino, M. A., Cruz, E. P., & Rivera, D. S. (2022). Real-time flood detection using image processing and predictive analytics. International Journal of Computer Applications and Disaster Management, 13(1), 23–38. [Google Scholar] [Crossref]
12. Zakaria, H., Liu, Y., & Chen, X. (2023). Remote water-level monitoring system for flood management using sensor networks and web technologies. Journal of Hydrological Engineering, 28(6), 1–14. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
- Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
- Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
- Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
- Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet