Deep Learning-Based Fishing Ground Prediction Using Asymmetric Spatiotemporal Scales: A Case Study of <i>Ommastrephes bartramii</i>

Deep Learning-Based Fishing Ground Prediction Using Asymmetric Spatiotemporal Scales: A Case Study of <i>Ommastrephes bartramii</i>

Selecting the optimal spatiotemporal scale in fishing ground prediction models can maximize prediction accuracy. Current research on spatiotemporal scales shows that they are symmetrically distributed, which may not capture specific oceanographic features conducive to fishing ground formation. Recen...

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Bibliographic Details
Main Authors: Mingyang Xie, Bin Liu, Xinjun Chen, Wei Yu, Jintao Wang
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Fishes
Subjects:
asymmetric spatiotemporal scale
center fishing ground
deep learning
<i>Ommastrephes bartramii</i>
U-Net
Online Access:https://www.mdpi.com/2410-3888/9/2/64

Internet

https://www.mdpi.com/2410-3888/9/2/64

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