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提高渔情预报技术是渔场学的重要研究内容,地理信息系统等高新技术在渔情预报中的应用日益增多。本文根据我国在西南大西洋海域生产的鱿钓统计数据,结合海洋环境因子,利用栖息地指数方法构建了西南大西洋阿根廷滑柔鱼中心渔场预报模型,自主研发了软件预报系统。同时,利用实时的表温、叶绿素和海面高度距平值等海洋环境因子,对2009年生产作业情况进行了验证。分析认为,实际作业渔场基本上都分布在栖息地指数为0.5以上的海域,1-4月份中心渔场预报准确率为57%～74%,平均准确率为68.29%。研究认为,栖息地指数模型可较为准确地用来预测阿根廷滑柔鱼中心渔场,同时依靠地理信息系统等技术实现了中心渔场预报的智能化。研究亮点:利用栖息地指数方法构建了基于表温、叶绿素和海面高度距平均值的阿根廷滑柔鱼中心渔场预报模型,并自主研发了软件预报系统。根据2009年1-4月实时的海洋环境和生产实际情况,对中心渔场进行了验证比较,其预报平均准确率达到68.29%。
The improvement of fishery forecasting technology is an important research content of fisheries science. The application of high-tech such as geographic information system in fishery forecasting is increasing day by day. Based on the statistical data of squid fishing produced in the Southwest Atlantic Ocean in China and the marine environmental factors, the paper uses the habitat index method to construct the fishing ground prediction model of the South Africa Atlantic Cathayphysis anguillarum and develops the software forecasting system independently. Meanwhile, the use of real-time surface temperature, chlorophyll and sea level anomalies and other marine environmental factors, production operations in 2009 were verified. The analysis shows that the actual operating fishing grounds are basically all distributed in the sea area with habitat index of 0.5 or above. The forecast accuracy of central fishing ground in January-April is 57% -74% with the average accuracy of 68.29%. The study suggests that the Habitat Index model can be used to predict more accurately the fishing ground of C. glomerata and realize the intelligence of the center fishery forecast by using the technologies such as GIS. Research highlights: Based on the Habitat Index method, a fishing ground prediction model based on the average of surface temperature, chlorophyll and sea surface height was established and a software forecasting system was independently developed. According to the real-time marine environment and production actual situation from January to April in 2009, the comparison and verification of the center fishing grounds shows that the average forecast accuracy is 68.29%.