A new model to forecast fishing ground of Scomber japonicus in the Yellow Sea and East China Sea

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• 作者：Feng Gao ; Xinjun Chen ; Wenjiang Guan ; Gang Li
• 关键词：Scomber japonicus ; environmental factors from remote sensing ; forecasting model of fishing ground ; Yellow Sea and East China Sea
• 刊名：Acta Oceanologica Sinica
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：35
• 期：4
• 页码：74-81
• 全文大小：1,080 KB
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• 作者单位：Feng Gao (1) (2) (3) (4)
  Xinjun Chen (1) (2) (3) (4)
  Wenjiang Guan (1) (2) (3) (4)
  Gang Li (1) (2) (3) (4)
  
  1. College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China
  2. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai, 201306, China
  3. National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai, 201306, China
  4. Collaborative Innovation Center for Distant-water Fisheries, Shanghai Ocean University, Shanghai, 201306, China
  
• 刊物主题：Oceanography; Climatology; Ecology; Engineering Fluid Dynamics; Marine & Freshwater Sciences; Environmental Chemistry;
• 出版者：Springer Berlin Heidelberg
• ISSN：1869-1099

文摘

The pelagic species is closely related to the marine environmental factors, and establishment of forecasting model of fishing ground with high accuracy is an important content for pelagic fishery. The chub mackerel (Scomber japonicus) in the Yellow Sea and East China Sea is an important fishing target for Chinese lighting purse seine fishery. Based on the fishery data from China’s mainland large-type lighting purse seine fishery for chub mackerel during the period of 2003 to 2010 and the environmental data including sea surface temperature (SST), gradient of the sea surface temperature (GSST), sea surface height (SSH) and geostrophic velocity (GV), we attempt to establish one new forecasting model of fishing ground based on boosted regression trees. In this study, the fishing areas with fishing effort is considered as one fishing ground, and the areas with no fishing ground are randomly selected from a background field, in which the fishing areas have no records in the logbooks. The performance of the forecasting model of fishing ground is evaluated with the testing data from the actual fishing data in 2011. The results show that the forecasting model of fishing ground has a high prediction performance, and the area under receiver operating curve (AUC) attains 0.897. The predicted fishing grounds are coincided with the actual fishing locations in 2011, and the movement route is also the same as the shift of fishing vessels, which indicates that this forecasting model based on the boosted regression trees can be used to effectively forecast the fishing ground of chub mackerel in the Yellow Sea and East China Sea.