Analysis of small pelagic fishing grounds using a generalized additive model in the Makassar Strait

Putri, Rini Sahni; Bibin, Muhammad; Surianti,; Hasrianti,; Damis,; Putri, Andi Rani Sahni; Wulandari, Ully; Wardani, Mentari Puspa

doi:10.1088/1755-1315/763/1/012042

Cited by 1 publication

(2 citation statements)

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“…The studies in [3], [5]- [9], [25], [48] used statistical approaches to predict the potential fishing zone. Nurdin et al [5] also used GAM to identify possible fishing zones and assess the influence of climate change on fish distribution using IPCC-AR5-RCP temperature projections.…”

Section: Discussionmentioning

confidence: 99%

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A review on features and methods of potential fishing zone

Ya’acob,

Nik Dzulkefli,

Abdul Aziz

et al. 2024

IJECE

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This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.

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Section: Discussionmentioning

confidence: 99%

“…A few researchers such as in studies [5], [9], [11], and [47], [48] used the statistical model of the GAM to assess the influence of temperature change and Chl-a on fish distribution. Nurdin et al [5] in their study set the goal of the study to find a connection between fish distribution and SST calculated by using the GAM.…”

Section: Generalized Additive Modelmentioning

confidence: 99%