An integrative framework for AI-supported coastal hydrodynamics monitoring and analysis

The intensification of climate change poses significant threats to coastal regions worldwide, manifesting in increased storm frequency, sea level rise, and consequent flooding risks. This study addresses the urgent need for innovative monitoring strategies by introducing an advanced coastal hazard monitoring system specifically designed for areas with underdeveloped monitoring infrastructure. Employing a blend of traditional methods and cutting-edge technologies, including the Segment Anything Model (SAM) for high-resolution image segmentation and Dynamic Mode Decomposition (DMD) for pattern recognition, we provide a comprehensive assessment of coastal water dynamics. The study highlights the application of SAM in identifying water-land boundary despite challenges such as image distortion and variable lighting conditions. Additionally, the innovative use of monoplotting with DEM provides a robust framework for accurate mapping in complex coastal terrains. This research advances our understanding of coastal dynamics under the impact of climatic changes and sets a new benchmark for environmental monitoring, offering substantial improvements over traditional methodologies by integrating technological advancements with practical fieldwork. The findings demonstrate significant implications for disaster preparedness and the sustainable management of coastal regions, emphasizing the necessity of adopting advanced technologies to enhance the resilience of vulnerable coastal communities against the escalating threats posed by climate change.