Flood Disaster Preparedness and Response Using a Web-Based Integrated Flood Management System (IFMS)
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Abstract
Floods are one of the most frequent and damaging natural hazards in Malaysia, especially in low-lying places, such as Batu Pahat and Johor. National flood monitoring systems, such as InfoBanjir and Malaysian Meteorological Department (MET) Malaysia, have disjointed data pools, late updates, and inadequate public access. In this paper, we present an IFMS, which is a contemporary web platform developed to integrate national flood management systems through data collection, automatic processing, dynamic visualization tools, and others. The system architecture consists of three main layers: IoT-enabled flood sensors, centralized web server, and responsive user interfaces. Backend processing is performed using Laravel, and front-end design uses Bootstrap and Chart.js for live data visualization. The IFMS algorithm classifies severity using a predefined standard for water levels and rainfall, modelled by a pseudocode for reproducibility and scalability. The real-time data are centralized in various APIs, such as data.gov.my and Google Maps, to ensure real-time updates occur throughout the time, and interactive monitoring by map. According to the experimental assessment, the IFMS achieves a less than one minute data refresh speed which outstrips the 15–30 min delay compared with the one observed by InfoBanjir. After user acceptance testing (UAT) (194 respondents) user satisfaction rate 94.9% for the system and technical stability 89.7% were achieved so that the new solution to be acceptable and operational. The first solution is evidenced by an evaluation comparison with other systems implemented globally, such as the Iowa Flood Information System (IFIS), Tokyo Metropolitan Flood Control System, and European Flood Awareness System (EFAS) which showed innovation in adopting real-time API integration, hydrograph and hyetograph visualization, and mobile responsiveness. Consequently, the IFMS represents an important advancement in the flood management landscape in Malaysia, harmonizing global standards with local deployment to contribute to greater situational awareness, decision-making, and community resilience.
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References
M. A. H. Saidi et al., “Integrating Local Knowledge into Floods Impact Assessment: A Case Study from Gemereh, Segamat, Johor,” e-BANGI Journal, vol. 22, no. 2, 2025.
H. S. Rosmadi, M. F. Ahmed, M. B. Mokhtar, and C. K. Lim, “Reviewing challenges of flood risk management in Malaysia,” Water, vol. 15, no. 13, p. 2390, 2023.
N. Josipovic, and K. Viergutz, “Smart solutions for municipal flood management: overview of literature, trends, and applications in German cities,” Smart Cities, vol. 6, no. 2, pp. 944–964, 2023, doi: 10.3390/smartcities6020046.
R. Turner, and C. Sun, “Near real-time responsive flood event representation: An open-source interactive web application architecture,” ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., vol. 10, pp. 365–372, 2024.
Z. S. Salim, and A. Z. A. Faisal, “INFOBANJIR: innovative geospatial technology for flood information,” 2024.
S. Veerappan, “Edge-enabled smart storm water drainage systems: A real-time analytics framework for urban flood management,” J. Smart Infrastruct. Environ. Sustain., vol. 1, no. 1, pp. 52–59, 2024.
M. Wajid et al., “Flood prediction system using IoT & artificial neural network,” VFAST Trans. Softw. Eng., vol. 12, no. 1, pp. 210–224, 2024.
M. S. H. Saad et al., “Integrated approach to flood risk management: A comprehensive thematic review in the Malaysia context,” Int. J. Eng. Technol. Natural Sci., vol. 6, no. 1, pp. 1–10, 2024.
K. D. Bathe and N. S. Patil, “Flood detection and mapping: A critical review of methods, challenges and future prospects,” J. Indian Soc. Remote Sens., pp. 1–32, 2025, doi: 10.1007/s12524-025-02277-x.
T. Q. Dang et al., “Integrating intelligent hydro-informatics into an effective early warning system for risk-informed urban flood management,” Environ. Model. Softw., vol. 183, p. 106246, 2025.
Z. N. Al-kateeb, and D. B. Abdullah, “Unlocking the potential: synergizing IoT, cloud computing, and big data for a bright future,” Iraqi J. Comput. Sci. Math., vol. 5, no. 3, p. 25, 2024.
C. Kirpalani, “Technology-driven approaches to enhance disaster response and recovery,” in Geospatial Technology for Natural Resource Management, 2024, pp. 25–81.
P. Chitra et al., “IoT and sensor-based flood monitoring and warning systems for disaster management,” in Proc. 2025 Int. Conf. Automation and Computation (AUTOCOM), IEEE, 2025.
Z. Sa’adi et al., “Evaluating flood early warning system and public preparedness and knowledge in urban and semi-urban areas of Johor, Malaysia: Challenges and opportunities,” Int. J. Disaster Risk Reduct., vol. 113, p. 104870, 2024.
I. Demir et al., Actionable Flood Warnings Based on Ground-truth Data to Support Iowa DOT BridgeWatch Platform Functionality. U.S. Dept. of Transportation, Federal Highway Administration, 2024.
A. A. Al-Omari et al., “Utilizing remote sensing and GIS techniques for flood hazard mapping and risk assessment,” Civil Eng. J., vol. 10, no. 5, pp. 1423–1436, 2024.
W. Sarinastiti et al., “Exploring crowdsourced data validation methods for flood mitigation: A comprehensive review,” in Proc. 2024 Int. Electron. Symp. (IES), IEEE, 2024.
W. Yin et al., “Harnessing game engines and digital twins: Advancing flood education, data visualization, and interactive monitoring for enhanced hydrological understanding,” Water, vol. 16, no. 17, p. 2528, 2024.
C. A. Grant et al., “Comprehensive assessment of flood risk and vulnerability for essential facilities: Iowa case study,” Urban Sci., vol. 8, no. 3, p. 145, 2024.
A. Sharma, M. Yadav, and D. Agarwal, “Cloud-based flood monitoring – A review,” in Proc. 2024 1st Int. Conf. Pioneering Develop. Comput. Sci. Digital Technol. (IC2SDT), IEEE, 2024.
Chatrabhuj et al., “Integration of remote sensing data and GIS technologies in river management system,” Discover Geoscience, vol. 2, no. 1, p. 67, 2024.
N. N. M. S. N. Mohd et al., “Development and implementation of an IoT-based early flood detection and monitoring system utilizing time series forecasting for real-time alerts in resource-constrained environments,” Malaysian J. Sci. Adv. Technol., pp. 30–36, 2025.
J. P. J. Peixoto et al., “A geospatial multi-domain flood prediction tool exploiting open datasets,” Software Impacts, vol. 21, p. 100697, 2024.
J. H. Park et al., “Development of an on-demand flooding safety system achieving long-term inexhaustible cooling of small modular reactors employing metal containment vessel,” Nucl. Eng. Technol., vol. 56, no. 7, pp. 2534–2544, 2024.
R. Sanjay, D. Pulakhandam, and V. Nirmalrani, “Real-time dashboarding using big data tools,” in Proc. 2024 Int. Conf. Inventive Comput. Technol. (ICICT), IEEE, 2024.