摘要： 为提高南太平洋长鳍金枪鱼渔场预测的性能，本研究基于2000—2015年的南太平洋长鳍金枪鱼所处的渔场时空信息、渔获数量、延绳钓钩数以及3种重要的环境因子：海面温度、海面高度以及叶绿素a浓度，提出了一种基于极限学习机（ELM）的渔场预测方法。该方法首先提出了一种新型的类独热编码算法，对渔场数据进行特征数字编码；然后通过构建基于ELM的金枪鱼渔场预测模型，并通过训练学习来自适应地获取预测模型的网络参数，实现了对金枪鱼渔场的智能预测。文中的试验表明，在使用2015年南太平洋长鳍金枪鱼数据作为测试验证时，该模型取得了84.07%的总体渔场预测准确率，同时F1 Score指数达到80.9%，与常规方法相比，提高了长鳍金枪鱼渔场预测的性能。本研究为渔场预报研究提供了一种新的思路。

关键词: 长鳍金枪鱼, 渔场预测, 极限学习机, 南太平洋

Abstract:  To improve the predicted performance of south Pacific Thunnus alalunga fisheries, in this paper a fishery prediction method based on the extreme learning machine (ELM) is proposed using the spatial and temporal information, the number of fish caught, the number of longline catches and three important environmental factors data: sea surface temperature, sea surface height and chlorophyll-a  concentration of Thunnus alalunga fishery in the South Pacific from 2000 to 2015. This method firstly introduces an one-hot liked coding algorithm, for effective digital coding of characteristics of several kinds of fisheries data, and then presents a Thunnus alalunga fishery forecasting model based on ELM, to learn the characteristics of various discretization encoded data and to obtain the stability prediction model parameters adaptively, for effective prediction of tuna fishing ground. Finally, the experimental results show that when the data of Thunnus alalunga in the South Pacific in 2015 are used as the test validation, the prediction accuracy of the model is 84.07% and the F1 Score index is 80.9%, which improves the performance of the fishery prediction compared with the conventional methods. This study provides a new idea for the research of fishery prediction.

Key words: Thunnus alalunga, fishing ground forecasting, extreme learning machine, the South Pacific