Advances in Real-Time Flood Forecasting and Early Warning Systems: Integrating Artificial Intelligence, Digital Twins, and Immersive Visualisation Technologies

Real-time flood forecasting and early warning systems (RTFF–EWS) have become central to contemporary flood risk management, particularly as climate change intensifies the frequency, magnitude, and spatial complexity of flood events. Recent advances demonstrate a clear shift from conventional physics-based forecasting towards integrated digital ecosystems that combine multi-source data acquisition, advanced analytics, artificial intelligence, and immersive decision-support interfaces. This study synthesises state-of-the-art tools and emerging research directions shaping next-generation RTFF–EWS.

On the data side, dense Internet of Things (IoT) sensor networks, remote sensing (radar, LiDAR, satellites, drones), and crowd-sourced information now enable near–real-time monitoring of hydrological and hydraulic states across urban and catchment scales. These heterogeneous data streams are increasingly fused through machine learning (ML) and deep learning (DL) frameworks, including recurrent neural networks, long short-term memory models, and hybrid physics-informed approaches, to enhance forecast lead time, accuracy, and robustness under data scarcity and uncertainty. Natural language processing (NLP) and large language–based pipelines (LLP) further extend RTFF capabilities by extracting actionable intelligence from unstructured data such as social media, emergency reports, and textual observations, improving situational awareness during rapidly evolving flood events.

Beyond forecasting, digital visualisation technologies are redefining how flood information is communicated and operationalised. Virtual reality (VR), augmented reality (AR), mixed reality (MR), and digital twins (DT) provide immersive and interactive representations of flood dynamics, impacts, and response options. These tools enable scenario testing, stakeholder engagement, and decision rehearsal across the full flood risk management cycle, from preparedness and response to recovery. Digital twins, in particular, are emerging as integrative platforms that couple real-time sensor data, predictive models, and visual interfaces into living representations of urban water systems. Despite these advances, key challenges remain, including data reliability, computational demands, interoperability across platforms, and the translation of complex model outputs into inclusive, actionable warnings for diverse stakeholders. This study also shows plausible deployment pathways for contemporary digital technologies, particularly NLP/LLP-enabled information extraction and AI-driven multi-agent frameworks, and demonstrates how their integration with RTFF–EWS can support adaptive, interpretable, and decision-centred flood risk management under real-world operational constraints.