基于机器学习的西南印度洋深海散射层声学资源密度预测
作者:
中图分类号:

S932.4

基金项目:

国家重点研发计划(2019YFD0901401)


Prediction on acoustic resource density of deep scattering layer of the southwestern Indian Ocean based on machine learning
Author:
  • WAN Shujie

    WAN Shujie

    College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China
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  • CHEN Xinjun

    CHEN Xinjun

    College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai 201306, China;Key Laboratory of Ocean Fisheries Exploitation, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
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Fund Project:

National Key R&D Program of China

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    摘要:

    预测大洋中深海散射层的资源丰度与分布,对指示海洋保护动物与重要渔场分布,开发散射层中的渔业资源等具有重要意义。本研究以海里面积散射系数(Nautical area scattering coefficient,NASC)作为散射层的资源密度指标,综合多个环境因子,利用K-means聚类和SSA-XGBoost模型,实现了对西南印度洋散射层资源密度的分类预测。结果表明,模型预测的准确率为80.51%,精确率为76%,召回率为78%,样本数据与预测数据的高密度区域空间分布相匹配,模型的应用效果较好。通过对2011年不同季节散射层密度的预测,发现散射层高密度区的重心由东南向西北方向移动,其中春季时期高密度区的重心的纬度最大,冬季时高密度区的重心的纬度最小,高密度区的重心点在东南-西北方向上的离散性大于东北-西南方向。本研究可为阐明散射层大尺度空间分布和资源变动规律提供新的方法。

    Abstract:

    Predicting the abundance and distribution of deep scattering layer is important to indicate the distribution of marine protected animals,important fishing grounds, and develop fishery resources into the scattering layer. This study used the Nautical Area Scattering Coefficient (NASC) as the resource density indicator of the scattering layer, and used K-means clustering and SSA-XGBoost model to predict the resource density of the scattering layer based on multiple environmental factors in the southwestern Indian Ocean. The results showed that the accuracy of the model prediction is 80.51%, the precision is 76%, and the recall is 78%. The sample data matches the high-density spatial distribution of the predicted data, and the application effect of the model is good. By predicting the density of the scattering layer in different seasons in 2011, it was found that the center of gravity in the high-density area of the scattering layer moved from southeast to northwest, with the latitude of the center of gravity being the largest in spring and the smallest in winter. The dispersion of the center of gravity in the southeast-northwest direction is greater than that in the northeast-southwest direction. This study can provide a new method for elucidating the distribution and resource variation patterns of scattering layers in larger spaces.

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万树杰,陈新军.基于机器学习的西南印度洋深海散射层声学资源密度预测[J].上海海洋大学学报,2024,33(6):1357-1368.
WAN Shujie, CHEN Xinjun. Prediction on acoustic resource density of deep scattering layer of the southwestern Indian Ocean based on machine learning[J]. Journal of Shanghai Ocean University,2024,33(6):1357-1368.

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  • 收稿日期:2023-10-07
  • 最后修改日期:2024-01-05
  • 录用日期:2024-03-11
  • 在线发布日期: 2024-12-05
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