Improvement of marine environment feature extraction based on deep convolution embedded clustering (DCEC) for fishery forecast model

Zhang Tianjiao; Liao Zhangze; Song Bo; Yuan Hongchun; Song Liming; Zhang Shanshan

doi:10.12284/hyxb2021072

Volume 43 Issue 8

Aug. 2021

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Zhang Tianjiao，Liao Zhangze，Song Bo, et al. Improvement of marine environment feature extraction based on deep convolution embedded clustering (DCEC) for fishery forecast model−A case study of bigeye tuna (Thunnus obesus) in the Southwest Indian Ocean[J]. Haiyang Xuebao，2021, 43(8)：105–117 doi: 10.12284/hyxb2021072

Citation:

Zhang Tianjiao，Liao Zhangze，Song Bo, et al. Improvement of marine environment feature extraction based on deep convolution embedded clustering (DCEC) for fishery forecast model−A case study of bigeye tuna (Thunnus obesus) in the Southwest Indian Ocean[J]. Haiyang Xuebao，2021, 43(8)：105–117 doi: 10.12284/hyxb2021072

Citation:

Zhang Tianjiao，Liao Zhangze，Song Bo, et al. Improvement of marine environment feature extraction based on deep convolution embedded clustering (DCEC) for fishery forecast model−A case study of bigeye tuna (Thunnus obesus) in the Southwest Indian Ocean[J]. Haiyang Xuebao，2021, 43(8)：105–117 doi: 10.12284/hyxb2021072

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Improvement of marine environment feature extraction based on deep convolution embedded clustering (DCEC) for fishery forecast modelA case study of bigeye tuna (Thunnus obesus) in the Southwest Indian Ocean

doi: 10.12284/hyxb2021072

1.
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
2.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
3.
China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 200135, China

Received Date: 2020-11-17
Rev Recd Date: 2021-01-22

Available Online: 2021-04-30

Publish Date: 2021-08-25

Abstract

Abstract

In order to improve the forecasting ability of the fishery forecast model for the longline bigeye tuna (Thunnus obesus), we proposed a marine environment feature extraction method based on deep convolutional embedded clustering (DCEC), combined with generalized additive model (GAM) for forecasting the longline bigeye tuna fishing grounds in the Southwest Indian Ocean. We used the MODIS-Aqua and MODIS-Terra sea surface temperature (SST) three-level inversion image data (in days) from January to December in 2018 at 0.041 6°×0.041 6° to construct a DCEC model, determined the optimal number of clusters based on the Davies-Bouldi index (DBI), and extracted the category feature value (F_M) of each month’s sea surface temperature (SST); we used monthly 1°×1° bigeye tuna longline fishery data from January to December in 2018 generated from the Indian Ocean Tuna Commission (IOTC), and calculated the catch per unit effort (CPUE); we matched the monthly category feature value F_M and the monthly average value of Chl a concentration with the CPUE data to construct an improved GAM; we matched the monthly average SST, the monthly average Chl a concentration and CPUE data to build a basic GAM; we used the joint hypothesis test (F test) to verify the influence of model explanatory variables; we used akaike information criterion (AIC), mean square error (MSE), and draw the frequency distribution diagrams and box diagrams of measured and predicted values, etc., to analysis the improvement effect of the improved GAM compared to the basic GAM. The results showed that: (1) the category feature value (F_M) extracted based on the DCEC model could better reflect the temporal and spatial dynamic characteristics of SST in the Southwest Indian Ocean, and was related with the climatic conditions, monsoon conditions, and hydrological characteristics in the Southwest Indian Ocean; (2) the factor interpretation of F_M was higher than that of the monthly average SST in GAM, which means F_M had more significant impact on the CPUE of bigeye tuna. The high catch rate was concentrated in the areas where the F_M category was 2, 10, 24 with intersections between the warm and cold currents; (3) the AIC of the improved GAM was reduced by 9.17% than that of the basic GAM and MSE of the improved GAM was reduced by 26.7% than that of the basic GAM; the frequency distribution of the CPUE logarithmic value predicted by the improved GAM was closer to the normal distribution, and the high frequency distribution interval was closer to that of the measured value; the scatter plot showed that the CPUE predicted by the improved GAM had a significant correlation with the measured CPUE, with r equaled to 0.60. This study proves the effectiveness of the DCEC model in extracting marine environmental features, and can provide a reference for the further study on the bigeye tuna fishery forecast.
- deep convolutional embedded clustering,
- marine environment feature extraction,
- bigeye tuna,
- Southwest Indian Ocean,
- fishery forecast,
- generalized additive model

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References(28)

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