Effects of restoration of coastal zones to mitigate the impact of climate change

Effects of restoration of coastal zones to mitigate the impact of climate change

Coastal flooding is a significant threat to communities, ecosystems, and infrastructure in low-lying coastal areas, driven by the increasing frequency and intensity of storm surges, sea-level rise, and extreme weather events. Effective early warning systems (EWS) are critical for mitigating the adverse impacts of these hazards by enabling timely evacuation and disaster response. Traditional EWS rely on numerical models, such as the XBeach model, to simulate hydrodynamic processes and predict flood extents. However, these models often require substantial computational resources, limiting their application in real-time scenarios.

Machine learning (ML) has emerged as a promising tool to address these limitations. By training predictive models on numerical simulation outputs, ML algorithms can significantly reduce computational time while maintaining accuracy. Despite this potential, the integration of numerical models and ML for developing coastal flooding EWS remains underexplored, particularly for small-scale, localized systems in vulnerable areas like Granelli in southeastern Sicily.

• Development of a Predictive Framework

• Model Validation

• Supporting Coastal Risk Management

• Numerical Simulations

• Machine Learning

• Monitoring Network

• Satellite Data Integration