Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean Environment
We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Fishes |
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| Online Access: | https://www.mdpi.com/2410-3888/9/3/81 |
| _version_ | 1827306212688920576 |
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| author | Tianjiao Zhang Jia Xin Wei Yu Hongchun Yuan Liming Song Zhuo Yang |
| author_facet | Tianjiao Zhang Jia Xin Wei Yu Hongchun Yuan Liming Song Zhuo Yang |
| author_sort | Tianjiao Zhang |
| collection | DOAJ |
| description | We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub><mo>)</mo></mrow></semantics></math></inline-formula> based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub></mrow></semantics></math></inline-formula> to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub></mrow></semantics></math></inline-formula> outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub></mrow></semantics></math></inline-formula> and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models. |
| first_indexed | 2024-04-24T18:17:41Z |
| format | Article |
| id | doaj.art-19ed84a4b39a49b08ce5ae4e2df0e7dd |
| institution | Directory Open Access Journal |
| issn | 2410-3888 |
| language | English |
| last_indexed | 2024-04-24T18:17:41Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fishes |
| spelling | doaj.art-19ed84a4b39a49b08ce5ae4e2df0e7dd2024-03-27T13:38:16ZengMDPI AGFishes2410-38882024-02-01938110.3390/fishes9030081Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean EnvironmentTianjiao Zhang0Jia Xin1Wei Yu2Hongchun Yuan3Liming Song4Zhuo Yang5College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaWe introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub><mo>)</mo></mrow></semantics></math></inline-formula> based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub></mrow></semantics></math></inline-formula> to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub></mrow></semantics></math></inline-formula> outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>F</mi></mrow><mrow><mi>M</mi></mrow></msub></mrow></semantics></math></inline-formula> and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models.https://www.mdpi.com/2410-3888/9/3/81deep convolutional embedded clusteringDavies–Bouldi indexmachine learningfishery forecast |
| spellingShingle | Tianjiao Zhang Jia Xin Wei Yu Hongchun Yuan Liming Song Zhuo Yang Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean Environment Fishes deep convolutional embedded clustering Davies–Bouldi index machine learning fishery forecast |
| title | Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean Environment |
| title_full | Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean Environment |
| title_fullStr | Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean Environment |
| title_full_unstemmed | Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean Environment |
| title_short | Predicting the Fishery Ground of Jumbo Flying Squid (<i>Dosidicus gigas</i>) off Peru by Extracting Features of the Ocean Environment |
| title_sort | predicting the fishery ground of jumbo flying squid i dosidicus gigas i off peru by extracting features of the ocean environment |
| topic | deep convolutional embedded clustering Davies–Bouldi index machine learning fishery forecast |
| url | https://www.mdpi.com/2410-3888/9/3/81 |
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