Forecasting fishing ground of <i>Thunnus alalunga</i> based on BP neural network in the South Pacific Ocean

Mao Jiangmei; Chen Xinjun; Yu Jing

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Mao Jiangmei, Chen Xinjun, Yu Jing. Forecasting fishing ground of Thunnus alalunga based on BP neural network in the South Pacific Ocean[J]. Haiyang Xuebao, 2016, 38(10): 34-43.

Citation:

Mao Jiangmei, Chen Xinjun, Yu Jing. Forecasting fishing ground of Thunnus alalunga based on BP neural network in the South Pacific Ocean[J]. Haiyang Xuebao, 2016, 38(10): 34-43.

Mao Jiangmei, Chen Xinjun, Yu Jing. Forecasting fishing ground of Thunnus alalunga based on BP neural network in the South Pacific Ocean[J]. Haiyang Xuebao, 2016, 38(10): 34-43.

Citation:

Mao Jiangmei, Chen Xinjun, Yu Jing. Forecasting fishing ground of Thunnus alalunga based on BP neural network in the South Pacific Ocean[J]. Haiyang Xuebao, 2016, 38(10): 34-43.

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Forecasting fishing ground of Thunnus alalunga based on BP neural network in the South Pacific Ocean

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;South China Sea Fisheries Research Institute, Chinese Academy of Fishery Science, Guangzhou 510300, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
3.
South China Sea Fisheries Research Institute, Chinese Academy of Fishery Science, Guangzhou 510300, China

Received Date: 2016-05-08

Abstract

Abstract

Albacore tuna (Thunnus alalunga) is an important target species for Chinese pelagic fishery in the South Pacific Ocean, and the accurate predicting of fishing ground can enhance catch efficiency and improve fishing operation. In this paper, we apply the BP artificial neural network model in the DPS (data processing system) to forecast fishing ground, according to the fishing yield statistics from longline, the sea surface temperature (SST), sea surface height (SST), and ENSO (El Niño-Southern Oscillation) index obtained by satellite remote sensing from 1993 to 2010. We consider the fishing yield (unit is the number of tuna) and CPUE (catch per unit of effort) as the character factors of fishing ground, the same as the output factors in BP artificial neural network respectively, and we take month, latitude, longitude, SST, SSH and ENSO index as the input factors, building the 4-3-1, 5-4-1, 5-3-1, 6-5-1, 6-4-1, 6-3-1 model and choosing the better results. It is found that the model used by CPUE is better than that model used by fishing yield, particularly the 6-4-1 model, and the relatively error is only 0.006 14, which can forecast the fishing ground of Thunnus alalunga in the South Pacific Ocean accurately.
- albacore tuna,
- artificial neural network,
- CPUE,
- fishing ground,
- South Pacific Ocean

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References

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