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A Machine Learning Technique for Deriving the Optimal Mesh Size of a Gizzard Shad (Konosirus punctatus) Gillnet
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A Machine Learning Technique for Deriving the Optimal Mesh Size of a Gizzard Shad (Konosirus punctatus) Gillnet
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Journal Article
A Machine Learning Technique for Deriving the Optimal Mesh Size of a Gizzard Shad (Konosirus punctatus) Gillnet
2024
Overview
Gizzard shads are facing a continual decline in population, necessitating the implementation of selective gear design for effective resource management. This study aims to prevent the bycatch of young gizzard shads, a non-target fish species, and to derive mesh sizes appropriate for fishery management. Experimental fishing (n = 11) was conducted by manufacturing gillnet fishing gear with different mesh sizes (50.5, 55.1, 60.6, and 67.3 mm) in the coastal waters of the southern Gyeongsang Province. Two methods were employed to estimate the appropriate mesh size of the shad gillnet as follows: firstly, by analyzing the selectivity curve based on body length data; secondly, by developing a complex machine learning model considering biological and economic factors. Model 1 was constructed using mesh variables to classify the score groups. As a result of this study, the total length with a 0.5 gillnet selection ratio. which was estimated to be 179.3, 195.6, 215.1, and 238.9 mm for the 50.5, 55.1, 60.6, and 67.3 mm mesh sizes, respectively. In Model 1, a mesh size of 57.85 mm or less was determined as the most appropriate mesh size. Therefore, considering both biological and economic aspects, shad gillnets should have a mesh size in the 50.5 to 55.1 mm range.
