Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network
In a marine environment, the concentration of chlorophyll is an important indicator of quality, which is also considered an indicator used to predict the marine ecological environment, which is further considered an important means of predicting red tide disasters. Although existing methods for pred...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/151 |
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| _version_ | 1832588228895440896 |
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| author | Yingying Jin Feng Zhang Xia Wang Lei Wang Kuo Chen Liangyu Chen Yutao Qin Ping Wu |
| author_facet | Yingying Jin Feng Zhang Xia Wang Lei Wang Kuo Chen Liangyu Chen Yutao Qin Ping Wu |
| author_sort | Yingying Jin |
| collection | DOAJ |
| description | In a marine environment, the concentration of chlorophyll is an important indicator of quality, which is also considered an indicator used to predict the marine ecological environment, which is further considered an important means of predicting red tide disasters. Although existing methods for predicting chlorophyll concentration have achieved encouraging performance, there are still two limitations: (i) they primarily focus on the correlation between variables while ignoring negative noise from non-predictive variables and (ii) they are unable to distinguish the impact of chlorophyll from that of non-predictive variables on chlorophyll concentration at future time points. In order to overcome these obstacles, we propose a Multi-Attention Collaborative Network (MACN)-based triangle-structured prediction system. In particular, the MACN consists of two branch networks, with one named <b>NP-net</b>, focusing on non-predictive variables, and the other named <b>T-net</b>, applied to the target variable. NP-net incorporates variable-distillation attention to eliminate the negative effects of irrelevant variables, and its outputs are used as auxiliary information for T-net. T-net works on the target variable, and both its encoder and decoder are related to NP-net to use the output of NP-net for assistance in learning and prediction. Two actual datasets are used in the experiments, which show that the MACN performs better than various kinds of state-of-the-art techniques. |
| format | Article |
| id | doaj-art-bbe3a07bdcad449586e01d3999bda212 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-bbe3a07bdcad449586e01d3999bda2122025-01-24T13:37:02ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113115110.3390/jmse13010151Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative NetworkYingying Jin0Feng Zhang1Xia Wang2Lei Wang3Kuo Chen4Liangyu Chen5Yutao Qin6Ping Wu7East Sea Information Center, State Oceanic Administration, Shanghai 200136, ChinaEast Sea Information Center, State Oceanic Administration, Shanghai 200136, ChinaSchool of Teacher Education, Shangqiu Normal University, Shangqiu 476000, ChinaEast China Sea Forecasting and Disaster Reduction Center, Ministry of Natural Resources, Shanghai 200136, ChinaEast Sea Information Center, State Oceanic Administration, Shanghai 200136, ChinaEast Sea Information Center, State Oceanic Administration, Shanghai 200136, ChinaEast China Sea Ecology Center, Ministry of Natural Resources, Shanghai 200136, ChinaEast China Sea Forecasting and Disaster Reduction Center, Ministry of Natural Resources, Shanghai 200136, ChinaIn a marine environment, the concentration of chlorophyll is an important indicator of quality, which is also considered an indicator used to predict the marine ecological environment, which is further considered an important means of predicting red tide disasters. Although existing methods for predicting chlorophyll concentration have achieved encouraging performance, there are still two limitations: (i) they primarily focus on the correlation between variables while ignoring negative noise from non-predictive variables and (ii) they are unable to distinguish the impact of chlorophyll from that of non-predictive variables on chlorophyll concentration at future time points. In order to overcome these obstacles, we propose a Multi-Attention Collaborative Network (MACN)-based triangle-structured prediction system. In particular, the MACN consists of two branch networks, with one named <b>NP-net</b>, focusing on non-predictive variables, and the other named <b>T-net</b>, applied to the target variable. NP-net incorporates variable-distillation attention to eliminate the negative effects of irrelevant variables, and its outputs are used as auxiliary information for T-net. T-net works on the target variable, and both its encoder and decoder are related to NP-net to use the output of NP-net for assistance in learning and prediction. Two actual datasets are used in the experiments, which show that the MACN performs better than various kinds of state-of-the-art techniques.https://www.mdpi.com/2077-1312/13/1/151chlorophyll concentration forecastingmulti-attention collaborativedeep neural networklong-term forecasting |
| spellingShingle | Yingying Jin Feng Zhang Xia Wang Lei Wang Kuo Chen Liangyu Chen Yutao Qin Ping Wu Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network Journal of Marine Science and Engineering chlorophyll concentration forecasting multi-attention collaborative deep neural network long-term forecasting |
| title | Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network |
| title_full | Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network |
| title_fullStr | Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network |
| title_full_unstemmed | Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network |
| title_short | Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network |
| title_sort | multi step forecasting of chlorophyll concentration with multi attention collaborative network |
| topic | chlorophyll concentration forecasting multi-attention collaborative deep neural network long-term forecasting |
| url | https://www.mdpi.com/2077-1312/13/1/151 |
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