Modeling vessel noise emissions through the accumulation and propagation of Automatic Identification System data
Author(s) -
Sarah T. V. Neenan,
Paul R. White,
T.G. Leighton,
Peter J. Shaw
Publication year - 2016
Publication title -
proceedings of meetings on acoustics
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.15
H-Index - 16
ISSN - 1939-800X
DOI - 10.1121/2.0000338
Subject(s) - noise (video) , automatic identification system , computer science , identification (biology) , noise pollution , noise control , environmental science , data modeling , transit (satellite) , meteorology , real time computing , noise reduction , engineering , geography , transport engineering , artificial intelligence , public transport , botany , database , image (mathematics) , biology
Recent research has demonstrated the importance of soundscape characterization, modeling, and mapping with regard to their potential to highlight noise levels that can adversely affect fish behavior. Models and noise maps are seen as valuable tools for generating comprehensive information at relatively low costs; a model-based approach presents a powerful and cost-effective way to evaluate noise levels. This research aims to develop a vessel noise modeling method using Automatic Identification System (AIS) and online data. The vessel noise map is produced using estimated source levels of individual ships at each AIS transmission point along a vessel transit line. The accumulation and propagation of these transit line emissions, in 1 km grid squares, produces an ocean shipping noise map showing average received levels over the desired time period. The results show temporal and spatial differences in vessel noise emissions, with summer months nosier than winter months, and coastal areas and known shipping channels much nosier than the open ocean. Unlike many previous models, this approach uses individual vessel source emissions, and is very computationally efficient even for large datasets.Recent research has demonstrated the importance of soundscape characterization, modeling, and mapping with regard to their potential to highlight noise levels that can adversely affect fish behavior. Models and noise maps are seen as valuable tools for generating comprehensive information at relatively low costs; a model-based approach presents a powerful and cost-effective way to evaluate noise levels. This research aims to develop a vessel noise modeling method using Automatic Identification System (AIS) and online data. The vessel noise map is produced using estimated source levels of individual ships at each AIS transmission point along a vessel transit line. The accumulation and propagation of these transit line emissions, in 1 km grid squares, produces an ocean shipping noise map showing average received levels over the desired time period. The results show temporal and spatial differences in vessel noise emissions, with summer months nosier than winter months, and coastal areas and known shipping c...
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