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Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.

Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.

Basisinformatie over ‘Open Access to scholarly information'.

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one publication added to basket [331391]
A new machine learning approach to seabed biotope classification Cooper, K.M.; Barry, J. (2020). A new machine learning approach to seabed biotope classification. Ocean Coast. Manag. 198: 105361. https://dx.doi.org/10.1016/j.ocecoaman.2020.105361 In: Ocean & Coastal Management. Elsevier Science: Barking. ISSN 0964-5691; e-ISSN 1873-524X, meer

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Beschikbaar in	Auteurs
VLIZ: Open access 355738 [ download pdf ]

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Trefwoorden

Aquatic communities > Benthos Classification Clustering Habitat > Biotopes Mapping

Author keywords

Macrofauna, Machine learning, K-means, R shiny

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Auteurs		Top
Cooper, K.M., meer Barry, J.

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Abstract

Effective management in the marine environment requires a thorough understanding of the distribution of natural resources, including that of the benthos, the animals living in and on the seabed. Hitherto, it has been difficult to identify broadscale patterns in the benthos as the faunal clusters identified from individual surveys are not directly comparable. As a result, much reliance has been placed on one-off broadscale spatial surveys or matching samples to a common set of biotopes. In this study, new benthic macrofaunal data from discrete surveys are matched to existing broadscale cluster groups identified using unsupervised machine learning (k-means). This objective approach allows for continual improvements in our understanding of macrofaunal distribution patterns, thereby supporting ongoing conservation and marine spatial planning efforts. Other benefits are discussed. Finally, an R shiny web application is presented, allowing users to biotope match their own data.

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