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基于深度学习和时空特征融合的海洋渔船密度预测方法
丁依婷,胡志远,董帝渤
0
(福建社会科学院博士后创新实践基地,福建 福州,350001;福建师范大学理论经济学博士后科研流动站,福建 福州,350117;福建理工大学智慧海洋与工程研究院,福建 福州,350118)
摘要:
为了从海量渔船轨迹数据中挖掘隐含的信息和知识,进而为渔业行政主管部门的决策提供科学依据,本研究以AIS渔船轨迹数据为研究对象,提出了一种基于深度学习和面向时空特征融合的海洋渔船密度预测方法:首先,利用渔船轨迹数据集对渔船行驶区域进行网格划分;其次,筛选出渔船高密度区域进行研究,避免数据稀疏性问题;再次,根据渔船轨迹数据的时空分析,构建三维时空融合矩阵;最后,通过卷积循环神经网络模型捕获渔船分布的时间和空间特征,并利用卷积神经网络的堆叠加强对空间特征的学习。实验通过东海海域渔船真实轨迹数据进行具体测试,结果表明渔船密度预测值与真实值非常接近,平均绝对误差为4×10-4,模型较好地拟合了渔船密度分布特征,有效地提高了渔船捕捞热点预测的准确性和鲁棒性。
关键词:  渔业资源  渔船密度预测  深度学习  卷积神经网络
DOI:10.3969/J.ISSN.2095-4972.20230421002
基金项目:福建省海洋灾害基础调查与评估项目([3500]MZZJ[GK]2022003);福建省财政科研资助项目(KY030293)
A density prediction method for fishing vessel based on deep learning and fusion of spatial-temporal features
DING Yiting,HU Zhiyuan,DONG Dibo
(Postdoctoral Innovation Practice Base, Fujian Academy of Social Sciences, Fuzhou 350001, China;Postdoctoral Scientific Research Station of Theoretical Economics, Fujian Normal University, Fuzhou 350117, China;Institute of Smart Marine and Engineering, Fujian university of Technololy, Fuzhou 350118, China)
Abstract:
To mine hidden information from massive AIS trajectory data and provide a scientific basis for decision-making of marine fishery management departments, this paper proposes a marine fishing vessel density prediction method based on deep learning and fusion of spatial-temporal features. Firstly, the driving area of fishing vessels is grided according to fishing vessel trajectory dataset. Secondly, high-density fishing vessel areas are selected for study to avoid data sparsity. Thirdly, the fishing vessel distribution data is constructed into a three-dimensional matrix of spatial and temporal fusion. Finally, the convolutional recurrent neural network model is used to capture spatial and temporal features, while the convolutional neural network is stacked to enhance the learning of spatial features. The experiment was specifically tested with real fishing vessel trajectory data of the East China Sea. Results showed that the predicted values of fishing vessel density were very close to the true values, with an average absolute error of 4×10-4. It indicates that the model can better fit the distribution characteristics of fishing vessel density, which improve effectively the accuracy and robustness of fishing hotspot prediction.
Key words:  fisheries resources  fishing vessel density prediction  deep learning  convolutional recurrent neural network

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