EGU24-18150, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comprehensive Flood Early Warning Systems: From Modelling to Policy Making Perspectives

Kourosh Behzadian1,5, Farzad Piadeh2, Saman Razavi3,4, Luiza Campos5, Mohamad Gheibi6,8, and Albert Chen7
Kourosh Behzadian et al.
  • 1School of Computing and Engineering, University of West London, London, W5 5RF, UK (
  • 2Centre for Engineering research, School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, UK
  • 3Department of Civil, Environmental and Geological Engineering, University of Saskatchewan, Saskatoon, Canada
  • 4Institute for Water Futures, Mathematical Science Institute, Australian National University, Canberra, Australia
  • 5Dept of Civil, Environmental and Geomatic Engineering, University College London, Gower St, London WC1E 6BT
  • 6Civil and Environmental Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
  • 7College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, Streatham Campus, N Park Rd, Exeter EX4 4QF, UK
  • 8Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Studentská 1402/2, 461 17 Liberec, Czech Republic

Todays, early warning systems are widely applied in real-time flood forecasting operations as valuable non-structural tools for mitigating the impacts of floods [1].  Although many research works have perfectly could review recent advances in this era, current review papers tend to focus narrowly on specific perspectives, such as water quantity or quality [2]. Therefore, there is a pressing need for a more comprehensive and multi-disciplinary approach that not only explores various potential aspects of flood early warning system applications but also reveals the interconnections between these aspects [3]. This paper aims to bridge this gap by mapping out diverse applications and presenting significant trends, past initiatives, and future directions across a wide range of domains. By adopting such an approach, our goal is to provide a more holistic understanding of flood early warning systems and pave the way for further exploration in this critical field.

This papers, as state-of-art, suggests that a comprehensive framework may include all these aspects to meet all desired task and also ensure that all aspect of sustainability, reliability, resiliency, and accuracy have been fulfilled: (1) using recent input data extracted from both well known resources such as ground station and satellite stations, and novel but local resources i.e. IoT-based remote sensing, drones, USV and even social media and qualitative data; (2) Advance modelling with focusing on hybrid deep learning and physics-informed neural networks for different type of flood i.e. fluvial, pluvial or surface run-off. Also, application of data mining for data screening still have required more attention; (3) Adding concept of water quality as target and outputs of EWS especially with focusing on emerging pollutants, biological pollutants and micro-plastics; (4) Interconnection of EWS with optimisation techniques, decision support systems, and multi criteria decision making processes; (5) Appropriate sensitivity/uncertainty analysis especially due to requirement for developing dynamic retrainable or self-trainable EWS; (6) Application of post modelling tools including virtual/augmented/mixed reality or digital twin to including stakeholder engagement.


[1] Piadeh, F., Behzadian, K., Chen, A.S., Kapelan, Z., Rizzuto, J., Campos, L.C. (2023). Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining. Water Research, 247, p.120791.

[2] Piadeh, F., Behzadian, K., Chen, A.S., Campos, L.C., Rizzuto, J., Kapelan, Z. (2023). Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling. Environmental Modelling & Software, 167, p.105772.

[3]  Ringo, J., Sabai, S., Mahenge, A. (2024). Performance of early warning systems in mitigating flood effects. A review. Journal of African Earth Sciences, 210, p.105134.

How to cite: Behzadian, K., Piadeh, F., Razavi, S., Campos, L., Gheibi, M., and Chen, A.: Comprehensive Flood Early Warning Systems: From Modelling to Policy Making Perspectives, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18150,, 2024.