EGU25-3526, updated on 14 Mar 2025
https://doi.org/10.5194/egusphere-egu25-3526
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Tuesday, 29 Apr, 14:00–15:45 (CEST), Display time Tuesday, 29 Apr, 08:30–18:00
 
vPoster spot A, vPA.22
Community-based flood early warning system: Current practice and Future directions
Arghavan Panahi1, Nafiseh Karkhaneh1, and Farzad Piadeh2
Arghavan Panahi et al.
  • 1Iran university of science and technology, Civil engineering, Iran
  • 2Centre for Engineering research, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, UK

Social media applications have emerged as reliable communication channels, especially when traditional methods falter [1]. Their integration into emergency management presents significant advantages, including enhanced situational awareness during unfolding events, rapid dissemination of news and alerts to broader audiences, and improved coordination among decision-makers and stakeholders [2]. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring [3]. To better understand these advantages and challenges, a comprehensive review and analysis of the literature on the application of social media in this field was conducted.  Social media facilitates participatory and collaborative structures, enabling collective knowledge-building in public information and warning systems. To realise this vision, the authors examined, 73 studies conducted from 2014 to 2024 to systematically evaluate the current literature surrounding communication on social media and the latest research in social media informatics related to disasters. This review identified key challenges within existing studies. The articles included 23 related to pluvial floods, 12 related to fluvial floods, 17 related to storm floods and 21 paper that were unspecified The majority of the studies were conducted in China, followed by the United States. Various software platforms, including Twitter, YouTube, and other social media networks, were analysed. Data extraction from these platforms was performed using Python programming. The study periods ranged from 1 to 3,650 days. These findings serve as guidance for researchers examining the relationship between social media and disaster management. They aim to develop the use of social networks during disasters, analyse patterns, and create programming to identify best practices for utilising social media in times of crisis. In the future, a mapping framework and tool can be developed to automatically extract information from social media through text and image analysis. By integrating this data with other available information sources, it will be possible to generate more accurate inundation maps in real-time. It is essential to recognise that information about floods obtained from social media may be incomplete during communication interruptions. To address this issue, future research should prioritise integrating big data from urban Internet of Things networks and improving communication infrastructure repairs. By adopting this strategy, we can collect more comprehensive disaster information to enhance flood emergency response effectiveness.

References

[1] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

[2] Piadeh, F., Ahmadi, M., Behzadian, K. (2020). A Novel Planning Policy Framework for the Recognition of Responsible Stakeholders in the of Industrial Wastewater Reuse Projects. Journal of Water Policy, 24 (9), pp. 1541–1558.

[3] Bakhtiari, V., Piadeh, F., Chen, A., Behzadian, K. (2024). Stakeholder Analysis in the Application of Cutting-Edge Digital Visualisation Technologies for Urban Flood Risk Management: A Critical Review. Expert Systems with Applications, p.121426.

How to cite: Panahi, A., Karkhaneh, N., and Piadeh, F.: Community-based flood early warning system: Current practice and Future directions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3526, https://doi.org/10.5194/egusphere-egu25-3526, 2025.