外文摘要：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. Recent studies have shown that deep learning is a promising research direction for addressing spatiotemporal scale issues. In the era of big data, deep learning outperforms traditional methods by more accurately and efficiently mining high-value, nonlinear information. In this study, taking Ommastrephes bartramii in the Northwest Pacific as an example, we used the U-Net model with sea surface temperature (SST) as the input factor and center fishing ground as the output factor. We constructed 80 different combinations of temporal scales and asymmetric spatial scales using data in 1998-2020. By comparing the results, we found that the optimal temporal scale for the deep learning fishing ground prediction model is 15 days, and the spatial scale is 0.25 degrees x 0.25 degrees. Larger time scales lead to higher model accuracy, and latitude has a greater impact on the model than longitude. It further enriches and refines the criteria for selecting spatiotemporal scales. This result deepens our understanding of the oceanographic characteristics of the Northwest Pacific environmental field and lays the foundation for future artificial intelligence-based fishery research. This study provides a scientific basis for the sustainable development of efficient fishery production.
外文关键词：deep learning;U-net;asymmetric spatiotemporal scale;center fishing ground;Ommastrephes bartramii
作者：Xie, Mingyang;Liu, Bin;Chen, Xinjun;Yu, Wei;Wang, Jintao
作者单位：Minist Nat Resources;Shanghai Ocean Univ
期刊名称：FISHES
期刊影响因子：0.0
出版年份：2024
出版刊次：9（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台