基于贝叶斯概率的印度洋大眼金枪鱼渔场预报

Fishing ground forecasting of bigeye tuna in the Indian ocean based on bayesian probability model

本文采用贝叶斯概率为模型基础框架,利用来自印度洋金枪鱼管理委员会(IOTC)的大眼金枪鱼延绳钓历史渔获统计数据和美国国家海洋大气管理局(NOAA)的海温最优插值再分析数据,进行适用于印度洋金枪鱼延绳钓渔场的模型参数估算与预报模型构建。模型回报精度验证结果表明,印度洋大眼金枪鱼延绳钓渔场综合预报的准确率达到了65.96%。模型预报结果用概率百分比来表示,符合渔业资源分布的客观特点。利用中分辨率成像光谱仪MODIS提供的SST产品进行业务化运行的渔场预报,利用模型结果每周生成印度洋大眼金枪鱼延绳钓渔场概率预报图,用不同大小的圆形来表示渔场概率的高低,可以为印度洋区域的远洋渔业生产提供信息支持。

Based on the framework of Bayesian Probability model, historic statistic data of bigeye tuna longline catch in the Indian Ocean from IOTC and optimum interpolation sea surface temperature data from satellite remote sensing from NOAA were used to estimate the model prediction parameters for the Indian Ocean tuna longline fisheries. The validation results of the model showed that the forecasting accuracy of the model for the Indian Ocean bigeye tuna longline fisheries reached 65.96%. The Moderate Resolution Imaging Spectroradiometer （MODIS） SST products were used to do cyclical fishery forecasting in one week cycle and to provide information to support the production of offshore fishing in the Indian Ocean region.