Fishing ground forecasting of chub mackerel in the Yellow Sea and East China Sea using boosted regression trees

Gao Feng; Chen Xinjun; Guan Wenjiang; Li Gang

doi:10.3969/j.issn.0253-4193.2015.10.004

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Gao Feng, Chen Xinjun, Guan Wenjiang, Li Gang. Fishing ground forecasting of chub mackerel in the Yellow Sea and East China Sea using boosted regression trees[J]. Haiyang Xuebao, 2015, 37(10): 39-48. doi: 10.3969/j.issn.0253-4193.2015.10.004

Citation:

Gao Feng, Chen Xinjun, Guan Wenjiang, Li Gang. Fishing ground forecasting of chub mackerel in the Yellow Sea and East China Sea using boosted regression trees[J]. Haiyang Xuebao, 2015, 37(10): 39-48. doi: 10.3969/j.issn.0253-4193.2015.10.004

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Fishing ground forecasting of chub mackerel in the Yellow Sea and East China Sea using boosted regression trees

doi: 10.3969/j.issn.0253-4193.2015.10.004

College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;National Distance-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China

Received Date: 2014-12-16

Abstract

Abstract

To improve the accuracy of fishing ground forecasting of chub mackerel (Scomber japonicus) in the Yellow and East China Sea,and reduce the fishery production cost,a new fishing ground forecasting model based on boosted regression trees was proposed in this study. Model was fitted with data extracted from electronic logbooks of Chinese mainland large-type lighting purse seine fishery for chub mackerel,with a range from 2003 to 2010. The fishing area with fishing effort was identified as fishing ground and the pseudo non fishing ground data was randomly collected from background field,which is the fishing areas with no records in the logbooks. The predictive variables were sea surface temperature and other environmental factors. The performance of prediction of the model was evaluated with the testing dataset consist of actual fishing locations of year 2011. The results of the evaluation showed that the prediction model had a high prediction performance with an AUC value of 0.897. The results of spatial prediction showed that the predicted fishing ground and its shifting were coincided with the actual fishing locations,which indicated that the forecasting model based on boosted regression trees can be used to forecasting the fishing ground of chub mackerel in the Yellow and East China Sea.
- boosted regression trees,
- chub mackerel,
- fishing ground forecasting,
- Yellow Sea

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References(35)

References

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