Review of flood monitoring and prevention approaches: a data analytic perspective

Syed Asad Shabbir Bukhari (), Imran Shafi (), Jamil Ahmad (), Santos Gracia Villar (), Eduardo Garcia Villena (), Tahir Khurshaid () and Imran Ashraf ()
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Syed Asad Shabbir Bukhari: National University of Sciences and Technology (NUST)
Imran Shafi: National University of Sciences and Technology (NUST)
Jamil Ahmad: Abasyn University, Islamabad Campus
Santos Gracia Villar: Universidad Europea del Atlantico
Eduardo Garcia Villena: Universidad Europea del Atlantico
Tahir Khurshaid: Yeungnam University
Imran Ashraf: Yeungnam University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 5, No 3, 5103-5128

Abstract: Abstract Floods are among the most destructive natural disasters, causing significant loss of life, property damage, and disruption to communities. The necessity for creative and practical flood monitoring and prevention technologies has been highlighted in recent years by the frequency and intensity of floods that are growing. Flood management uses cutting-edge technology and approaches, as well as, their potential to increase disaster resilience. This study reviews flood monitoring and prevention strategies from a data analytic perspective, particularly those involving the Internet of Things (IoT), and machine learning. The utilization of IoT, data analytics, and machine learning tools within cutting-edge solutions facilitates real-time data collection, predictive modeling, and informed decision-making. With the help of community involvement and potential catastrophe, resilience improved and safeguarded the people, and property damages in flood-prone areas. Techniques for flood monitoring are explored including remote sensing, IoT, ground-based solutions, machine learning, and early flood alert systems concerning their processes involving data acquisition, data integration, scalability, real-time monitoring, infrastructure, and accuracy. In addition, current challenges are these approaches are discussed and future research directions are outlined. Key findings indicate the integration of these technologies to enhance disaster resilience by providing real-time monitoring and early warning systems, hence drastically reducing the impact caused by floods. This paper presents flood monitoring techniques through remote sensing, IoT, ground-based solutions, machine learning, and early flood alert systems in such aspects as data acquisition, integration, scalability, real-time monitoring, infrastructure, and accuracy. These techniques, however, are all still bedeviled by challenges such as high implementation costs, maintenance difficulties, and the feature of a robust communication infrastructure. In this regard, research in the future will be directed to cost-effectiveness in solution designing, improving the accuracy of predictive models, and wider engagement involving the community in flood risk management.

Keywords: Internet of Things; Natural disaster; Flood warning; Machine learning; Wireless sensor networks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-024-07050-w

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