•1 min read•from Frontiers in Marine Science | New and Recent Articles
Regularized log-linear models for predicting underwater radiated noise spectra of small vessels
Our take
Underwater radiated noise (URN) from vessels significantly impacts marine ecosystems, making the estimation of URN levels essential for effective environmental assessment. While predictive models exist for various vessel types, there is a notable gap for small recreational vessels, which are major contributors to noise pollution in coastal regions. This study addresses this gap by utilizing a comprehensive dataset of acoustic measurements from recreational vessels. We develop regularized log-linear regression models to derive length and speed-dependent source spectra, enhancing understanding of URN emissions between 23–24,000 Hz.
Underwater radiated noise (URN) from vessels is an anthropogenic stressor that negatively affects marine ecosystems. Accordingly, the ability to estimate URN levels is a critical component for environmental assessment. Predictive models are widely used to estimate URN emissions from different vessel types and operating conditions; however, comparable models for small recreational vessels are largely lacking, even though these vessels are arguably a dominant source of noise pollution in coastal areas. Here, we use a large dataset of acoustic measurements of recreational vessels to extend the empirical framework to small motorized vessels (motorized yachts and sailboats) by constructing regularized log-linear regression models that provide length and speed dependent source spectra over 23–24,000 Hz.
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Tagged with
#autonomous underwater vehicles#marine science#marine biodiversity#environmental DNA#marine life databases#underwater radiated noise#small vessels#URN emissions#recreational vessels#marine ecosystems#environmental assessment#noise pollution#motorized vessels#predictive models#acoustic measurements#log-linear models#regression models#coastal areas#motorized yachts#source spectra