Data

Four-class hardness prediction grids in the eastern Joseph Bonaparte Gulf of Timor Sea

Geoscience Australia
Li, J. ; Tran, M. ; Siwabessy, J.
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=https://pid.geoscience.gov.au/dataset/ga/90645&rft.title=Four-class hardness prediction grids in the eastern Joseph Bonaparte Gulf of Timor Sea&rft.identifier=https://pid.geoscience.gov.au/dataset/ga/90645&rft.publisher=Geoscience Australia&rft.description=This dataset contains four-class hardness (i.e., hard-1, hard-soft-2, soft-3 and soft-hard-4) prediction data from seabed mapping surveys on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf of the Timor Sea. This dataset was generated based on hard90 seabed hardness classification scheme using random forest methods based on the point data of seabed hardness classification using video images and multibeam data. Refer to Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness for further information on processing techniques applied [1]. [1] Li, J., Tran, M., Siwabessy, J., 2016. Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness PLOS ONE 11(2) e0149089.Maintenance and Update Frequency: unknownStatement: A prediction-based classification is produced using the Random Forest method based on bathymetry, backscatter data and their derivatives, with support from video. The prediction accuracy of hard, hard-soft, soft-hard and soft seabed types achieved a total classification accuracy of 90% based on 10-fold cross-validation. Based on the strong performance of the predictive model, the Random Forest was also used to predict the distribution of hard and soft seabed types across the four study areas.&rft.creator=Li, J. &rft.creator=Tran, M. &rft.creator=Siwabessy, J. &rft.date=2016&rft.coverage=westlimit=129.451; southlimit=-12.287; eastlimit=130.063; northlimit=-10.285&rft.coverage=westlimit=129.451; southlimit=-12.287; eastlimit=130.063; northlimit=-10.285&rft_rights=&rft_rights=Creative Commons Attribution 4.0 International Licence&rft_rights=CC-BY&rft_rights=4.0&rft_rights=http://creativecommons.org/licenses/&rft_rights=WWW:LINK-1.0-http--link&rft_rights=Australian Government Security ClassificationSystem&rft_rights=https://www.protectivesecurity.gov.au/Pages/default.aspx&rft_rights=WWW:LINK-1.0-http--link&rft_rights=Creative Commons Attribution 4.0 International Licence http://creativecommons.org/licenses/by/4.0&rft_subject=geoscientificInformation&rft_subject=Data Package&rft_subject=Marine&rft_subject=seabed&rft_subject=Data&rft_subject=EARTH SCIENCES&rft_subject=Published_External&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International Licence
http://creativecommons.org/licenses/by/4.0

Creative Commons Attribution 4.0 International Licence

CC-BY

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http://creativecommons.org/licenses/

WWW:LINK-1.0-http--link

Australian Government Security ClassificationSystem

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Brief description

This dataset contains four-class hardness (i.e., hard-1, hard-soft-2, soft-3 and soft-hard-4) prediction data from seabed mapping surveys on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf of the Timor Sea. This dataset was generated based on hard90 seabed hardness classification scheme using random forest methods based on the point data of seabed hardness classification using video images and multibeam data. Refer to Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness for further information on processing techniques applied [1]. [1] Li, J., Tran, M., Siwabessy, J., 2016. Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness PLOS ONE 11(2) e0149089.

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Maintenance and Update Frequency: unknown
Statement: A prediction-based classification is produced using the Random Forest method based on bathymetry, backscatter data and their derivatives, with support from video. The prediction accuracy of hard, hard-soft, soft-hard and soft seabed types achieved a total classification accuracy of 90% based on 10-fold cross-validation. Based on the strong performance of the predictive model, the Random Forest was also used to predict the distribution of hard and soft seabed types across the four study areas.

Issued: 2016

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130.063,-10.285 130.063,-12.287 129.451,-12.287 129.451,-10.285 130.063,-10.285

129.757,-11.286

text: westlimit=129.451; southlimit=-12.287; eastlimit=130.063; northlimit=-10.285

Subjects
Data | Data Package | Earth Sciences | Marine | Published_External | geoscientificInformation | seabed |

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uri : https://d28rz98at9flks.cloudfront.net/90645/90645_data.zip

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