Multi-Sensor Based Nearshore Bathymetry Projection in Rocky Coastlines Using Machine Learning Techniques

Accurate characterisation of nearshore bathymetry is crucial for modelling waves, monitoring hazards, and managing coasts. 

Ship-based technologies produce accurate bathymetry measurements but are expensive, time-consuming, and dangerous to use in relatively shallow coastal terrain, areas with rock outcrops, and intense waves. This results in a lack of bathymetry data in shallow coasts, creating a data void in nearshore zones. 

Several studies have used remotely sensed imagery for inverting nearshore bathymetry in shallow, homogeneous sandy coastal systems. However, existing empirical models often fail to estimate accurate bathymetry in energetic rock coast environments. To date, little or no attention has been placed on deriving nearshore bathymetry in rock coastlines due to high turbidity and heterogeneous bottom substrates. Empirical models do not perform effectively in determining nearshore water depth, which explains the greater focus on soft-coast systems and the neglect of rocky coastlines.

Multispectral (MS) optical sensors are preferred for bathymetry studies. The blue and green bands of the optical satellite image are limited in depth by turbidity, large waves, and currents. In contrast, Synthetic Aperture Radar (SAR) can track surface roughness and infer water depths in active waves and swells. By integrating passive optical and active SAR data, the limitations of relying solely on multispectral images to derive bathymetry in complex coastal areas can be overcome.

Rocky coasts are characterised by morphological heterogeneity that promotes increased turbulence under wave forcing. This affects light penetration and increases the complexity of optical water properties, reducing the accuracy of projected nearshore water depth using only optical sensors. While the western seaboard of Ireland is dominated by hard, rocky, cliffed coastline and extreme wave climates, it has a significant data gap in nearshore bathymetry.  

In a multi-sensor approach, this research used machine learning techniques to combine multispectral Sentinel-2 and Sentinel-1 SAR data with multibeam data to provide a comprehensive dataset for projecting nearshore bathymetry. This was carried out using supervised machine learning to train known depth values in a given coastal area, and unsupervised machine learning to predict unknown water depths in another nearshore zone. Projected water depth was validated using single-beam sonar data collected in Ballard Bay.

Random Forest, XGBoost, and LightGBM, techniques were used to train models. Models were then applied to generate nearshore bathymetry maps at three locations on the west coast of Ireland: Ballard Bay, Farrihy Bay, and Loop Head. The results indicated that Random Forest outperformed the XGBoost and LightGBM models across all sites, with R² values of 0.782, 0.765, and 0.740, respectively, with corresponding Root Mean Square Error (RMSE) of 0.578m, 0.698m, and 0.756m. A stacking ensemble model was built by combining the three models, which improved the R² for bathymetry prediction by 10% at all sites.

This research represents one of the first applications of machine learning–based nearshore bathymetry reconstruction focused specifically on rocky coastlines. The proposed ensemble method can produce precise bathymetric maps for nearshore areas across diverse regions and time periods. This will enable frequent assessment of rock coast evolution in relation to potential climate-driven impacts.