Integrated Flood Risk Early Warning for Adaptive Emergency Management: The IFloPhy Framework Coupling Machine...

With the increasing global risk of floods, there is an urgent need for new adaptive emergency management (AEM) frameworks. This study aims to integrate machine learning, physical models (such as the Variable Infiltration Capacity model and InfoWorks-ICM), and social data to develop the IFloPhy (Integrated Machine Learning and River Physical Model) framework, which explores early flood risk warnings under AEM. The multidimensional integration design of IFloPhy overcomes the limitations of traditional single-warning systems, enhancing dynamic response capabilities and predictive accuracy. By integrating physical processes, IFloPhy can dynamically track the formation and development of floods, comprehensively considering natural and socio-economic factors, thereby achieving holistic and interactive flood risk assessments. The incorporation of real-time satellite data with multi-model forecast results establishes an immediate warning mechanism, significantly reducing prediction uncertainty. IFloPhy has been deployed and validated in the San Isabel Basin in South America, demonstrating exceptional performance in areas with scarce data and limited communication infrastructure. IFloPhy offers new technologies and insights for risk management and AEM, proposing novel methods for flood risk emergency management.