Deep learning-based fishing ground prediction with multiple environmental factors

扫码查看

原文链接

万方数据

外文摘要：Improving the accuracy of fishing ground prediction for oceanic economic species has always been one of the most con-cerning issues in fisheries research.Recent studies have confirmed that deep learning has achieved superior results over traditional methods in the era of big data.However,the deep learning-based fishing ground prediction model with a single environment suffers from the problem that the area of the fishing ground is too large and not concentrated.In this study,we developed a deep learning-based fishing ground prediction model with multiple environmental factors using neon flying squid(Ommastrephes bartramii)in Northwest Pacific Ocean as an example.Based on the modified U-Net model,the approach involves the sea surface temperature,sea surface height,sea surface salinity,and chlorophyll a as inputs,and the center fishing ground as the output.The model is trained with data from July to November in 2002-2019,and tested with data of 2020.We considered and compared five temporal scales(3,6,10,15,and 30 days)and seven multiple environmental factor combinations.By comparing different cases,we found that the optimal temporal scale is 30 days,and the optimal multiple environmental factor combination contained SST and Chl a.The inclusion of multiple factors in the model greatly improved the concentration of the center fishing ground.The selection of a suitable combination of multiple environmental factors is beneficial to the precise spatial distribution of fishing grounds.This study deepens the understanding of the mechanism of environmental field influence on fishing grounds from the perspective of artificial intelligence and fishery science.

外文关键词：

Deep learningCenter fishing groundMultiple environmental factorsTemporal scalesU-NetOmmastrephes bartramii

作者：

Mingyang Xie、Bin Liu、Xinjun Chen

展开 >

作者单位：

College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China

Key Laboratory of Marine Ecological Monitoring and Restoration Technologies,Ministry of Natural Resources,Shanghai 200137,China

Key Laboratory of Oceanic Fisheries Exploration,Ministry of Agriculture and Rural Affairs,Shanghai Ocean University,Shanghai 201306,China

National Engineering Research Center for Oceanic Fisheries,Shanghai Ocean University,Shanghai 201306,China

Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources,Ministry of Education,Shanghai Ocean University,Shanghai 201306,China

展开 >

出版年：

2024

DOI：

10.1007/s42995-024-00222-4

海洋生命科学与技术(英文)

ISSN：

年,卷(期)：2024.6(4)