Coastal erosion, defined as the loss of coastal lands due to the net removal of material near the shoreline, poses significant hazards to people, infrastructure, the environment, and cultural heritage. The coast is a critical junction for environmental settings, human activities, infrastructure, and sensitive ecosystems, making coastal erosion a pressing concern.
Ireland’s coastline features both rock-dominated and soft sediment coasts, with erosion rates varying markedly according to the geological setting and the overall energy regime of the coast. Particularly vulnerable are the unconsolidated, soft, and low-lying coasts, which are already experiencing erosion, flooding, and ecosystem losses. Dublin’s coastline is primarily underlain by limestones, shales, and more resistant granites and metasediments, with soft sediments largely covering coastal areas.
Coastal erosion is influenced by global processes such as sea-level rise (SLR) and local conditions. Climate change exacerbates these issues, leading to increased erosion, flooding, and ecosystem losses. Rising sea levels and more frequent extreme sea-level events are expected to have profound impacts on communities and infrastructure along the Irish coast. Projections indicate that these changes could affect up to 2 million people living within 5 km of the coast and critical infrastructure.
The project aims to utilize data available on the GSI’s website (and Sentinel satellite images) to build a machine learning model that can predict coastal erosion. Students participating in this project need strong programming skills and a solid understanding of statistics.
The project consists of two main tasks:
- Data Integration: Combine time series data on wave height, wave period, tidal levels, and sediment transport rates, which are major factors influencing coastal erosion.
- Model Development: Build a functional data regression model to predict coastal erosion.
This project offers students a valuable opportunity to apply their programming and statistical skills to a real-world problem with significant environmental and societal impacts.