Abstract:  Due to the complex environment of the fishery in the south Pacific, it is difficult for traditional methods to extract effective features from the fishery impact factor data. A fusion deep learning model CNN-GRU-Attention is proposed to realize the prediction of CPUE (catch per unit effort) in the Thunnus alalunga fishery in the south Pacific. First CNN (convolution neural network) is utilized to extract characteristic data of impact factor within the scope of a single fishery, so as to obtain one dimensional characteristic data matching the CPUE value of the fishery, then GRU (gated recurrent unit) is used to predict CPUE value of the fishery, and finally Attention mechanism is used to accelerate the convergence of the model and optimize the model by weight allocation, and the same data is used to conduct comparative experiments on different models. The results show that: compared with the traditional prediction model multi-layer back-propagation (BP) networks, the absolute error decreases by 0.047 and the root-mean-square error decreases by 0.352. Compared with GRU, the absolute error is 0.012 lower and the root-mean-square error is 0.055 lower. The experimental results prove the effectiveness of CNN-GRU-Attention model and provide a new idea for the method of prediction of fishery.

Key words:  prediction of fishery, deep learning, CPUE

摘要： 南太平洋渔场环境复杂，传统的渔情预测方法难以从渔场影响因子数据中提取有效特征。提出一种融合深度学习模型CNN-GRU-Attention，以实现对南太平洋长鳍金枪鱼（Thunnus alalunga）渔场单位捕捞努力量渔获量（CPUE）的预测。首先利用卷积神经网络（CNN）提取单个渔场范围内影响因子特征数据，得到与渔场CPUE值相匹配的一维特征数据，然后通过门控循环（GRU）实现对渔场CPUE值的预测，最后使用Attention机制，利用权值分配加速模型收敛，实现对模型的优化，并利用相同数据在不同模型上进行对比试验。结果显示：与传统预测模型多层前馈网络（BP）相比，绝对误差降低0.047，均方根误差降低0.352；与GRU相比，绝对误差降低0.012，均方根误差降低0.055。试验结果证明了CNN-GRU-Attention模型的有效性，为渔情预测方法提供了一种新的思路。

关键词: 渔情预测, 深度学习, 单位捕捞努力量渔获量