Flood Alert Mapper: A Community-Powered Real-Time Flood Reporting and Rescue Location System

Flooding remains one of the most destructive natural disasters affecting communities, particularly in urban and low-lying areas where delayed reporting and inaccurate information hinder effective emergency response operations. Traditional flood monitoring systems often rely on centralized reporting mechanisms that may not provide real-time situational awareness and community-based verification. This study aimed to develop a Flood Alert Mapper: A Community-Powered Real-Time Flood Reporting and Rescue Location System capable of supporting real-time flood monitoring, AI-assisted validation, community participation, and emergency notification dissemination. The study utilized a developmental-descriptive research design and adopted the Rapid Application Development (RAD) model in designing and implementing the proposed system. The developed web-based platform integrated real-time flood mapping, crowdsourced flood reporting, AI-assisted confidence scoring using MobileNetV2, community validation mechanisms, and automated notification features. The system computed report reliability using a weighted confidence formula combining AI-generated analysis and community validation results.

AI-assisted Validation, Community Reporting, Disaster Management, Emergency Notification System

1. Abella, A. P., & Enriquez, M. (2024). Community-based flood alert system using long-range technology for Brgy. San Agustin, San Jose, Occidental Mindoro. Mindoro Journal of Social Sciences and Development Studies, 1(2), 27–34. https://journal.omsc.edu.ph/index.php/mjssds/article/view/10 [Google Scholar] [Crossref]

2. Abon, C. C., David, C. P. C., & Tabios, G. Q., III. (2012). Community-based monitoring for flood early warning system: An example in central Bicol River Basin, Philippines. Disaster Prevention and Management: An International Journal, 21(1), 85–96. https://doi.org/10.1108/09653561211202728 [Google Scholar] [Crossref]

3. Aguirre, A. (2025). Development of an IoT-based flood monitoring and early warning system with real-time web dashboard and SMS notification. Scribd. https://www.scribd.com/document/884201022/Development-of-an-IoT-Based-Flood-Monitoring-and-Early-Warning-System-With-Real-Time-Web-Dashboard-and-SMS-Notification [Google Scholar] [Crossref]

4. Association of Southeast Asian Nations Committee on Disaster Management. (2024). Strengthening ASEAN multi-hazard end-to-end early-warning system for natural disasters: An assessment of current capacity. Pacific Disaster Center. https://doi.org/10.13140/RG.2.2.18422.61769 [Google Scholar] [Crossref]

5. Chitwatkulsiri, D., Miyamoto, H., Irvine, K. N., Pilailar, S., & Loc, H. H. (2022). Development and application of a real-time flood forecasting system (RTFlood System) in a tropical urban area: A case study of Ramkhamhaeng Polder, Bangkok, Thailand. Water, 14(10), 1641. https://doi.org/10.3390/w14101641 [Google Scholar] [Crossref]

6. Daang, C. S., Go, D. M. L., Pagaduan, J. M. A., Santos, T. A. S., Vinluan, C. D. P., & De Leon, M. P. (2023). Hydrologic and hydraulic simulation of flooding in areas along Ilugin River and Buli Creek in Pasig, Philippines. In Research Congress Proceedings 2023 – SEE-11. De La Salle University. https://www.dlsu.edu.ph/wp-content/uploads/pdf/conferences/research-congress-proceedings/2023/SEE-11.pdf [Google Scholar] [Crossref]

7. Derouin, S. (2024). Alerting communities to hyper-localized urban flooding. Eos. https://doi.org/10.1029/2024EO240172 [Google Scholar] [Crossref]

8. Esparza, M., Farahmand, H., Liu, X., et al. (2024). Enhancing inundation monitoring of road networks using crowdsourced flood reports. Urban Informatics, 3, 25. https://doi.org/10.1007/s44212-024-00055-7 [Google Scholar] [Crossref]

9. Evangelio, C. T. (2024). Early warning system implementation in Banjo West, Tanauan, Batangas, Philippines. IDRIM Journal. https://www.idrimjournal.com/api/v1/articles/124778-early-warning-system-implementation-in-banjo-west-tanauan-batangas-philippines.pdf [Google Scholar] [Crossref]

10. Gersaniva, J. A., & Damasco, L. B., Jr. (2025). Innovative flood monitoring system: Enhancing disaster response and community safety. International Journal of Innovative Science and Research Technology, 10(6), 2901–2910. https://doi.org/10.38124/ijisrt/25jun1859 [Google Scholar] [Crossref]

11. Halim, I., Hamid, R., & Jamaluddin, M. (2024). Rapid flood warning system in recreational areas using LoRa-based sensor networks. Journal of Computing Research and Innovation. https://ir.uitm.edu.my/id/eprint/104107/1/104107.pdf [Google Scholar] [Crossref]

12. Hammood, W. A., Abdullah Arshah, R., Mohamad Asmara, S., Al Halbusi, H., Hammood, O. A., & Al Abri, S. (2021). A systematic review on flood early warning and response systems (FEWRS): A deep review and analysis. Sustainability, 13(1), 440. https://www.mdpi.com/2071-1050/13/1/440 [Google Scholar] [Crossref]

13. Islam, M. M., et al. (2025). Early warning systems in climate risk management: Roles and implementations in eradicating barriers and overcoming challenges. https://www.sciencedirect.com/science/article/pii/S2666592125000071 [Google Scholar] [Crossref]

14. Kamau, G. (2024). A real-time flood monitoring and alert system: Enhancing community resilience through technology integration – A case study of River Tana in Kenya. International Journal of Engineering and Science, 13(12), 73–80. https://theijes.com/papers/vol13-issue12/13127280.pdf [Google Scholar] [Crossref]

15. Khan, A., & Ilyas, M. (2021). Critical failure factors of flood early warning and response systems: A structured review and interpretive structural modelling analysis. Geosciences, 13(5), 137. https://www.mdpi.com/2076-3263/13/5/137 [Google Scholar] [Crossref]

16. Lagmay, A. M. F., Bagtasa, G., Andal, D. F., et al. (2024). An impact-based flood forecasting system for citizen empowerment. Asian Journal of Agriculture and Development, 21, 129–148. https://doi.org/10.22004/ag.econ.348355 [Google Scholar] [Crossref]

17. Mitkari, M. S., Suryakar, H., Mali, Y., Shaikh, Y., & Mullani, Z. (2023, May). Smart flood alert system using IoT. International Research Journal of Modernization in Engineering Technology and Science, 5(5). https://www.irjmets.com/uploadedfiles/paper/issue_5_may_2023/39881/final/fin_irjmets1684917924.pdf [Google Scholar] [Crossref]

18. Mostafiz, R. B., Rahim, M. A., Friedland, C. J., Rohli, R. V., Bushra, N., & Orooji, F. (2022). A data-driven spatial approach to characterize the flood hazard. Frontiers in Big Data, 5, 1022900. https://doi.org/10.3389/fdata.2022.1022900 [Google Scholar] [Crossref]

19. Muhd Zain, N., Elias, L. S., Paidi, Z., & Othman, M. (2020). Flood warning and monitoring system (FWMS) using GSM technology. Journal of Computing Research and Innovation, 5(1), 8–19. https://doi.org/10.24191/jcrinn.v5i1.158 [Google Scholar] [Crossref]

20. Najafi, H., Shrestha, P. K., Rakovec, O., Apel, H., Vorogushyn, S., Kumar, R., Thober, S., Merz, B., & Samaniego, L. (2024). High-resolution impact-based early warning system for riverine flooding. Nature Communications, 15, Article 3726. https://doi.org/10.1038/s41467-024-48065-y [Google Scholar] [Crossref]

21. Nigussie, L., Bekele, T. W., Haile, A. T., Mdee, A., Nicol, A., Cohen, J., Osei-Amponsah, C., Zeray Tedla, H., & Demissie, K. (2025). Does a citizen science approach enhance the effectiveness of flood early warning systems? Evidence from the Akaki catchment, Ethiopia. Citizen Science: Theory and Practice, 10(1). https://doi.org/10.5334/cstp.763 [Google Scholar] [Crossref]

22. Pandey, C. L., & Basnet, A. (2023). Challenges and prospects of flood early warning systems: A study of Narayani Basin. Asian Journal of Water, Environment and Pollution, 20(1), 17–24. https://doi.org/10.3233/AJW230004 [Google Scholar] [Crossref]

23. Pelone, B. C., & Arellano, A. J. P. (2024). Flood preparedness and utilization of early warning systems among households in selected flood-prone areas in Tagum City, Davao del [Google Scholar] [Crossref]

24. Norte. Davao Research Journal, 15(1), 35–49. https://doi.org/10.59120/drj.v15i1.149 [Google Scholar] [Crossref]

25. Ridwan, I. F. (2022). Internet of Things development for flood early warning monitoring system: A review. Journal of Computation Physics and Earth Science. https://journal.physan.org/index.php/jocpes/article/view/27 [Google Scholar] [Crossref]

26. Rizal, M., Warni, E., Angriawan, R., Hariadi, M., Arif, ., & Maulina, D. (2024). Design of flood early detection based on the Internet of Things and decision support system. Ingénierie des Systèmes d’Information. https://doi.org/10.18280/isi.290335 [Google Scholar] [Crossref]

27. Ruaro, E., & So, N. (2022). Analyzing weather patterns for predicting floods with regression models. Research Congress. De La Salle University Integrated School. https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1160&context=conf_shsrescon [Google Scholar] [Crossref]

28. Shah, A. A., Ullah, A., Khan, N. A., Khan, A., Tariq, M. A. U. R., & Xu, C. (2023). Community social barriers to non-technical aspects of flood early warning systems and NGO-led interventions: The case of Pakistan. Frontiers in Earth Science, 11. https://doi.org/10.3389/feart.2023.1068721 [Google Scholar] [Crossref]

29. Salgado, M. J., Alfonso, L., & Vélez Upegui, J. J. (2025). Towards integrating community and institutional flood early warning systems: A framework applied to an Andean tropical case. International Journal of Disaster Risk Reduction, 116(2), Article 105126. https://doi.org/10.1016/j.ijdrr.2024.105126 [Google Scholar] [Crossref]

30. Sunkara, V., Purri, M., Le Saux, B., & Adams, J. (2020). Street to cloud: Improving flood maps with crowdsourcing and semantic segmentation. arXiv. https://arxiv.org/abs/2111.06461 [Google Scholar] [Crossref]

31. Tao, Y., Tian, B., Adhikari, B. R., Zuo, Q., Luo, X., & Di, B. (2024). A review of cutting-edge sensor technologies for improved flood monitoring and damage assessment. Sensors, 24(21), 7090. https://www.mdpi.com/1424-8220/24/21/7090 [Google Scholar] [Crossref]

32. Tolentino, L. K. S. (2022). Real-time flood detection, alarm and monitoring system. Journal of Computing in Engineering and Applied Arts, 7(1). https://www.dlsu.edu.ph/wp-content/uploads/pdf/research/journals/jciea/vol-7-1/2tolentino.pdf [Google Scholar] [Crossref]

33. Turner, R., & Sun, C. (2024). Near real-time responsive flood event representation: An open-source interactive web application architecture. ISPRS Annals, X-4, 365–2024. https://isprs-annals.copernicus.org/articles/X-4-2024/365/2024/ [Google Scholar] [Crossref]

34. Vongkusolkit, J., Peng, B., Wu, M., Huang, Q., & Andresen, C. G. (2023). Near real-time flood mapping with weakly supervised machine learning. Remote Sensing, 15(13), 3263. https://doi.org/10.3390/rs15133263 [Google Scholar] [Crossref]

35. Yasmin, T., Khamis, K., Ross, A., Sen, S., Sharma, A., Sen, D., Sen, S., Buytaert, W., & Hannah, D. M. (2023). Brief communication: Inclusiveness in designing an early warning system for flood resilience. Natural Hazards and Earth System Sciences, 23(2), 667. https://doi.org/10.5194/nhess-23-667-2023 [Google Scholar] [Crossref]

36. Yuan, F., Fan, C., Farahmand, H., Coleman, N., Esmalian, A., et al. (2021). Smart flood resilience: Harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness. arXiv. https://arxiv.org/abs/2111.06461 [Google Scholar] [Crossref]

37. Zahri, R., Md Ali, A. H., Rambat, S., Ghazali, N. H., Ahmad, Y., & Hamzah, M. H. (2025). Flood early warning systems (FEWS) in enhancing disaster risk reduction and community resilience: A systematic review. International Journal of Law, Government and Communication. https://gaexcellence.com/ijlgc/article/view/5352 [Google Scholar] [Crossref]

• LeafQuest: A Mobile-Based Augmented Reality for Plant Placement, Discovery, and Growth
• Participatory Ergonomic Intervention Approach on Musculoskeletal Disorder (MSD) in Construction Sectors: A Systematic Review
• Integrating GIS into Traffic Incident Management: A Web-Based System
• RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop
• Educational Technology Course Design in Pre-Service Teachers Education: A Bibliometric Review of the Research Landscape