A neural network framework for extrapolating sea surface spectra from wave pressure signals
Year: 2025
Type: article
Abstract: This study investigates the prediction of sea surface elevation spectra from pressure measurements using frequency-specific artificial neural networks (ANNs). Unlike traditional time-domain approaches, the proposed methodology employs spectral analysis to decompose pressure signals into discrete frequency components and applies compressive sampling for outlier handling at the preprocessing stage. A separate ANN is trained for each frequency, utilising the water depth and other scalar features—including the statistical properties of the pressure head (mean, variance, peak period, and the narrow-bandedness parameter) - as inputs. The models are trained on 3005 samples, validated on 1252 samples, and tested on 751 samples. After model development, the ANNs were applied to a separate set of 100 previously unseen samples to evaluate the extrapolation framework. Model performance was assessed using a comprehensive set of error metrics. Results indicate that incorporating spectral features of pressure signals into ANN architectures provides a robust and efficient framework for sea surface spectra prediction, with potential applications in ocean and coastal engineering.
Source: Ocean Engineering
Institution: University of Reggio Calabria
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W4410642302