Improving detection of short-duration fishing behaviour in vessel tracks by feature engineering of training data

O’Farrell, Shay; Sanchirico, James N.; Chollett, Iliana; Cockrell, Marcy; Murawski, Steven A.; Watson, Jordan T.; Haynie, Alan; Strelcheck, Andrew; Perruso, Larry

doi:10.1093/icesjms/fsw244

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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Improving detection of short-duration fishing behaviour in vessel tracks by feature engineering of training data

2017
By O’Farrell, Shay ; Sanchirico, James N. ; Chollett, Iliana ; ...
Source: ICES Journal of Marine Science, 74(5), 1428-1436

[PDF-551.96 KB]

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Journal Title:

ICES Journal of Marine Science
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O’Farrell, Shay ; Sanchirico, James N. ; Chollett, Iliana ; Cockrell, Marcy ; Murawski, Steven A. ; ... More +

O’Farrell, Shay ; Sanchirico, James N. ; Chollett, Iliana ; Cockrell, Marcy ; Murawski, Steven A. ; Watson, Jordan T. ; Haynie, Alan ; Strelcheck, Andrew ; Perruso, Larry Less -
NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; AFSC (Alaska Fisheries Science Center) ; SEFSC (Southeast Fisheries Science Center) ; SER (Southeast Region)

NMFS (National Marine Fisheries Service) ; AFSC (Alaska Fisheries Science Center) ; SEFSC (Southeast Fisheries Science Center) ; SER (Southeast Region) Less -
Description:

Big data, such as vessel monitoring system (VMS) data, can provide valuable information on fishing behaviours. However, conventional methods of detecting behaviours in movement data are challenged when behaviours are briefer than signal resolution. We investigate options for improving detection accuracy for short-set fisheries using 581 648 position records from 181 vessels in the Gulf of Mexico bandit-reel fishery. We first investigate the effects of increasing VMS temporal resolution and find that detection accuracy improves with fishing-set duration. We then assess whether a feature engineering approach—in our case, changing the way pings are labelled when training a classifier—could improve detection accuracy. From a dataset of 12 184 observed sets, we find that the conventional point-labelling method results in only 49% of pings being correctly labelled as ‘fishing’, whereas a novel window-labelling method results in 88% of records being labelled as ‘fishing’. When the labelled data are used to train classifiers, point labelling attains true-positive/balanced-accuracy rates of only 37%/66%, whereas window labelling achieves 68%/83%. Finally, we map fishing distribution using the two methods, and show that point labelling underestimates the extent of fishing grounds by ∼33%, highlighting the benefits of window labelling in particular, and feature engineering approaches in general.
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Aquatic Science Ecology Ecology, Evolution, Behavior And Systematics Oceanography
Source:

ICES Journal of Marine Science, 74(5), 1428-1436
DOI:

https://doi.org/10.1093/icesjms/fsw244
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1054-3139;1095-9289;
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PDF
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Oxford University Press (OUP)
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Journal Article
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urn:sha256:5fb8ca5196ed4fb1bd9fc24050b4b6042bac6a53bb0e4021dad5fd085af61191
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https://repository.library.noaa.gov/view/noaa/51795/noaa_51795_DS1.pdf
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Improving detection of short-duration fishing behaviour in vessel tracks by feature engineering of training data

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