TY - JOUR

T1 - Predicting coastal wave conditions: A simple machine learning approach

AU - Roome, Edward

AU - Christie, David

AU - Neill, Simon

PY - 2024/10/28

Y1 - 2024/10/28

N2 - Accurate and reliable nearshore wave predictions are highly valuable for a range of marine activities, including coastal engineering and maritime transport. However, in nearshore locations, predicting wave properties is challenging due to complex shallow water processes, requiring local wave models. This article develops an alternative data-driven framework to predict wave parameters (e.g. significant wave height) through the extension of wave buoy datasets using a trained Gaussian process regression (GPR — a supervised machine learning method). We present an easy-to-implement workflow, where the extensive range of input parameters (from ECMWF’s (1) ERA5 reanalysis and (2) IFS forecast global wave model, resolution) drives the development of GPR models. At five contrasting locations around the United Kingdom’s coastline, the GPR models produce wave predictions (forecast and hindcast) with low bias scores and strong correlations with observations. When compared to the global (ERA5 reanalysis) and a benchmark shelf-scale (Atlantic-European North West Shelf reanalysis; AENWS, resolution) model, the GPR hindcasts reduced significant wave height () root-mean-squared error (RMSE) from 0.46 m (ERA5) and 0.21 m (AENWS) to 0.16 m (GPR). For the average zero-crossing wave period () RMSE reduced from 1.46 s (ERA5) and 1.15 s (AENWS) to 0.58 s (GPR). Because our approach uses publicly available global data, it can be implemented at any historic or active buoy location. We provide proof of concept for an online forecast and hindcast tool which has the potential to improve accessibility to coastal wave predictions for many marine stakeholders.

AB - Accurate and reliable nearshore wave predictions are highly valuable for a range of marine activities, including coastal engineering and maritime transport. However, in nearshore locations, predicting wave properties is challenging due to complex shallow water processes, requiring local wave models. This article develops an alternative data-driven framework to predict wave parameters (e.g. significant wave height) through the extension of wave buoy datasets using a trained Gaussian process regression (GPR — a supervised machine learning method). We present an easy-to-implement workflow, where the extensive range of input parameters (from ECMWF’s (1) ERA5 reanalysis and (2) IFS forecast global wave model, resolution) drives the development of GPR models. At five contrasting locations around the United Kingdom’s coastline, the GPR models produce wave predictions (forecast and hindcast) with low bias scores and strong correlations with observations. When compared to the global (ERA5 reanalysis) and a benchmark shelf-scale (Atlantic-European North West Shelf reanalysis; AENWS, resolution) model, the GPR hindcasts reduced significant wave height () root-mean-squared error (RMSE) from 0.46 m (ERA5) and 0.21 m (AENWS) to 0.16 m (GPR). For the average zero-crossing wave period () RMSE reduced from 1.46 s (ERA5) and 1.15 s (AENWS) to 0.58 s (GPR). Because our approach uses publicly available global data, it can be implemented at any historic or active buoy location. We provide proof of concept for an online forecast and hindcast tool which has the potential to improve accessibility to coastal wave predictions for many marine stakeholders.

KW - Artificial intelligence

KW - Machine learning

KW - Coastal wave prediction

KW - Wave buoy

U2 - 10.1016/j.apor.2024.104282

DO - 10.1016/j.apor.2024.104282

M3 - Article

VL - 153

JO - Applied Ocean Research

JF - Applied Ocean Research

SN - 0141-1187

M1 - 104282

ER -