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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.References1 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.
Behzadian et al. (Mon,) studied this question.