Predictive Modelling of Seabed Sediment Parameters Using Multibeam Acoustic Data: A Case Study on the Carnarvon Shelf, Western Australia  

Authors / CoAuthors

Huang, Z. | Nichol, S.L. | Siwabessy, P.J.W. | Daniell, J. | Brooke, B.P.

Abstract

Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam swath mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. The multibeam acoustic data at a ~1000 km2 area of the Carnarvon Shelf, Western Australia was used in a predictive modeling approach to map eight seabed sediment parameters. The modeling results indicates overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness, and Mean Grain Size. The study demonstrated that predictive modelling using the combination of machine learning models has several advantages over the interpolation of Cokriging. Combing multiple machine learning models can not only improve the prediction performance but also provides the ability to generate useful prediction uncertainty maps. Another important finding is that choosing an appropriate set of explanatory variables, through a manual feature selection process, is a critical step for optimizing model performance. In addition, machine learning models are able to identify important explanatory variables, which is useful in explaining underlying environmental process and checking prediction against existing knowledge of the study area. The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of co-variance of physical and biological data for this area. International Journal of Geographical Information Science

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nonGeographicDataset

eCat Id

70577

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Keywords

• External PublicationArticle

( Theme )

• bathymetry

( Theme )

• backscatter

( Theme )

• sedimentology

( Theme )

• marine

( Theme )

• CERF

• AU-WA

Australian and New Zealand Standard Research Classification (ANZSRC)

• Earth Sciences

• Published_Internal

Publication Date

2012-01-01T00:00:00

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Australian Government Security ClassificationSystem    

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Creative Commons Attribution 4.0 International Licence    

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notPlanned

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geoscientificInformation

Series Information

26, 283-307

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The paper was written based on the research results of CERF Marine Biodiversity Hub.

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CERF Marine Biodiversity Hub