Solar-Powered Iot-Based Barangay Drainage Canal Water Level Monitoring and Overflow Warning Simulation System Using ESP32 and Python Application

Localized flooding in urban community drainage systems remains a recurring environmental hazard in the Philippines, driven primarily by insufficient infrastructure maintenance and obstructed waterways. This special project presents the design, fabrication, and validation of a Solar-Powered IoT-Based Barangay Drainage Canal Water Level Monitoring and Overflow Warning Simulation System, an independent engineering solution tailored for community-level flood preparedness. The system architecture is split into a standalone Power Track—utilizing a 6V/6W polycrystalline solar panel, a TP4056 charging module, two 3.7V/3500mAh Lithium-Ion batteries in parallel, and an LM2596S buck converter—and a wireless Data Telemetry Track driven by an ESP32 microcontroller and an HC-SR04 ultrasonic sensor. Real-time acoustic reflection distances are parsed over a localized network socket interface to a standalone Python Tkinter desktop application named HydroSense System. Empirical testing within a simulated glass aquarium drainage model demonstrated a 100% functional success rate for telemetry transmission at 115200 baud, with no data packet drops. The software successfully categorized water levels into Normal, Warning, and Danger thresholds, instantly updating color-coded GUI alert themes (Green, Yellow, and Red) while simultaneously driving physical LED arrays and a passive buzzer alarm. Furthermore, an automated background engine flawlessly appended timestamped logs to local CSV spreadsheets for community record-keeping. The findings prove that integrating sustainable energy harvesting with localized IoT frameworks delivers an affordable, power-autonomous, and dependable early-warning system that significantly enhances local barangay disaster risk management without grid reliance or complex server overhead.

Internet of Things (IoT), ESP32, Python Tkinter, Flood Early Warning System, Solar Energy Harvesting, Disaster Risk Reduction

1. Al-Shareeda, M. A., Ali, A. M., Hammoud, M. A., Kazem, Z. H. M., & Hussein, M. A. (2025). Secure IoT-based real-time water level monitoring system using ESP32 for critical infrastructure. Journal of Cyber Security and Risk Auditing, 2025(2), 44–52. https://doi.org/10.63180/jcsra.thestap.2025.2.4 [Google Scholar] [Crossref]

2. Bagal, S. A., Bhisikar, T., Nimje, A., Nandanwar, Y., & Patil, T. (2023). Design of automatic domestic water quality monitoring & distribution control. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 11(4). https://doi.org/10.17148/IJIREEICE.2023.11405 [Google Scholar] [Crossref]

3. Bowler, A. L., Bakalis, S., & Watson, N. J. (2022). A review of in-line and on-line measurement technologies to support process monitoring and control in food manufacturing. Foods, 11(16), 2294. https://doi.org/10.3390/foods11162294 [Google Scholar] [Crossref]

4. Cadeliña, M. R. L.,&Perin, M. A. D. (2022). Flood alarm: Arduino-based water level indicator with buzzer for flood warning purposes. [Google Scholar] [Crossref]

5. Castro Garcia, L., Wang, J., Wang, S., Qiao, X., Kuhl, S., Zhang, H., Rangel de la Tejera, R., & Reed, D. (2025). I-Canopy: A resilient IoT platform for adaptive environmental monitoring at the edge. Smart Agricultural Technology, 12, 101611. https://doi.org/10.1016/j.atech.2025.101611 [Google Scholar] [Crossref]

6. Diriyana, A., Darusalam, U., & Natasha, N. D. (2019). Water level monitoring and flood early warning using microcontroller with IoT based ultrasonic sensor. Jurnal Teknik Informatika C.I.T Medicom, 11, 22–28. https://doi.org/10.35335/cit.Vol11.2019.9.pp22-28 [Google Scholar] [Crossref]

7. Fernandes, J. A. T., Loureiro, G. F., & Giovanelli, L. B. (2024). Development of a prototype for water reservoir level monitoring using an ESP32 board. International Journal of Geoscience, Engineering and Technology, 10(1), 46–53. https://doi.org/10.70597/ijget.v10i1.545 [Google Scholar] [Crossref]

8. Hanan, Gunawan, A. A. N., & Sumadiyasa, M. (2019). Water level detection system based on ultrasonic sensors HC-SR04 and ESP8266-12 modules with Telegram and buzzer communication media. Instrumentation Mesure Métrologie, 18(3). https://doi.org/10.18280/i2m.180311 [Google Scholar] [Crossref]

9. Lei, J.-F. (2010). Application of water-level monitoring system to modern management of drainage system. China Water&Wastewater, 26(18), 145–147. [Google Scholar] [Crossref]

10. Lengkong, A. G., Salaki, D. T.,&Alfonsius, E. (2024). Implementation of an Internet of Things (IoT)-based water level early warning system with Telegram notification. https://doi.org/10.33365/jatika.v6i2.349 [Google Scholar] [Crossref]

11. Lias, J., & Kumaran, G. (2024). IoT-based flood monitoring system using ESP32. Evolution in Electrical and Electronic Engineering. [Google Scholar] [Crossref]

12. Nur, I., & Shrestha, K. K. (2017). An integrative perspective on community vulnerability to flooding in cities of developing countries. Procedia Engineering, 198, 958–967. https://doi.org/10.1016/j.proeng.2017.07.141 [Google Scholar] [Crossref]

13. Oluremi, D., Chinedu, P., & Adeyemo, D. O. (2025). Optimizing solar energy harvesting in IoT nodes using deep self-organizing maps. ResearchGate Preprint. [Google Scholar] [Crossref]

14. Perumal, P., Raj, A., Bharathi, B., Raju, G. M., & Yogeswari, K. (2017). UAV assisted automated remote monitoring and control system for smart water bodies. In 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) (pp. 269–273). IEEE. https://doi.org/10.1109/ICRTCCM.2017.40 [Google Scholar] [Crossref]

15. Rahman, M. H., Shihab, M., Rahman, M. A., & Naderuzzaman, M. (2026). ESP32 microcontroller: A review of architecture, communication protocols, applications and research challenges. Open Access Journal on Engineering Applications, 1(2), 19–33. https://doi.org/10.64886/oajea.0102.00 [Google Scholar] [Crossref]

16. Sahani, S., et al. (2026). A high-efficiency inductor-less solar energy harvesting system for IoT end nodes. Results in Engineering. https://doi.org/10.1016/j.rineng.2026.110342 [Google Scholar] [Crossref]

17. Salman, K., Ahmed, S. T., Acharya, T., Annamalai, A., & Chouikha, M. (2026). Robust smart charging framework for resilience IoT deployments in remote environments. In 2025 IEEE Symposium on Wireless Technology&Applications (ISWTA). https://doi.org/10.1109/ISWTA68114.2025.11329928 [Google Scholar] [Crossref]

18. Varela, B. D., Tan, S. M., Jr., Jao, E. B., Jr., Espiña, M. B., & De Asis, C. A. (2022). Assessment of drainage conditions in the Poblacion barangays of Catarman, Northern Samar: A pilot study. International Journal of Environment and Climate Change, 12(10), 1337–1345. https://doi.org/10.9734/ijecc/2022/v12i1031156 [Google Scholar] [Crossref]

19. Velliangiri, A. (2025). Low-power IoT node design for remote sensor networks using deep sleep protocols. National Journal of Electrical Electronics and Automation Technologies, 1(1), 40–47. https://doi.org/10.17051/JEEAT/01.01.05 [Google Scholar] [Crossref]

20. Zhmud, V., Trubin, V., Dimitrov, L., & Karpov, E. (2018). Educational robotics: From simple sensors to complex robotic systems. Proceedings of the International Conference on Robotics and Automation. [Google Scholar] [Crossref]

21. Zulhakim, A. M., Abdullah, W. F. H., Abdul Halim, I. S., Haji Mamat, R., Muslan, M. I. A., & Ahmad, N. S. (2023). Smoothing sensor data in a controlled IoT framework with moving averages. In 2023 IEEE Regional Symposium on Micro and Nanoelectronics (RSM) (pp. 153–156). IEEE. https://doi.org/10.1109/RSM60397.2023.1049754 [Google Scholar] [Crossref]

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