Enhancing aquaculture water quality forecasting using novel adaptive multi-channel spatial-temporal graph convolutional network
In recent years, aquaculture has developed rapidly, especially in coastal and open ocean areas. In practice, water quality prediction is of critical importance. However, traditional water quality prediction models face limitations in handling complex spatiotemporal patterns. To address this challeng...
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| Vydáno v: | International journal of agricultural and biological engineering Ročník 18; číslo 1; s. 279 - 291 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Beijing
International Journal of Agricultural and Biological Engineering (IJABE)
01.02.2025
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| ISSN: | 1934-6344, 1934-6352 |
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| Abstract | In recent years, aquaculture has developed rapidly, especially in coastal and open ocean areas. In practice, water quality prediction is of critical importance. However, traditional water quality prediction models face limitations in handling complex spatiotemporal patterns. To address this challenge, a prediction model was proposed for water quality, namely an adaptive multi-channel temporal graph convolutional network (AMTGCN). The AMTGCN integrates adaptive graph construction, multi-channel spatiotemporal graph convolutional network, and fusion layers, and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data. Onsite aquaculture water quality data and the metrics MAE, RMSE, MAPE, and R2 were collected to validate the AMTGCN. The results show that the AMTGCN presents an average improvement of 34.01%, 34.59%, 36.05%, and 17.71% compared to LSTM, respectively; an average improvement of 64.84%, 56.78%, 64.82%, and 153.16% compared to the STGCN, respectively; an average improvement of 55.25%, 48.67%, 57.01%, and 209.00% compared to GCN-LSTM, respectively; and an average improvement of 7.05%, 5.66%, 7.42%, and 2.47% compared to TCN, respectively. This indicates that the AMTGCN, integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network, could provide an efficient solution for water quality prediction in aquaculture. |
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| AbstractList | In recent years, aquaculture has developed rapidly, especially in coastal and open ocean areas. In practice, water quality prediction is of critical importance. However, traditional water quality prediction models face limitations in handling complex spatiotemporal patterns. To address this challenge, a prediction model was proposed for water quality, namely an adaptive multi-channel temporal graph convolutional network (AMTGCN). The AMTGCN integrates adaptive graph construction, multi-channel spatiotemporal graph convolutional network, and fusion layers, and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data. Onsite aquaculture water quality data and the metrics MAE, RMSE, MAPE, and R2 were collected to validate the AMTGCN. The results show that the AMTGCN presents an average improvement of 34.01%, 34.59%, 36.05%, and 17.71% compared to LSTM, respectively; an average improvement of 64.84%, 56.78%, 64.82%, and 153.16% compared to the STGCN, respectively; an average improvement of 55.25%, 48.67%, 57.01%, and 209.00% compared to GCN-LSTM, respectively; and an average improvement of 7.05%, 5.66%, 7.42%, and 2.47% compared to TCN, respectively. This indicates that the AMTGCN, integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network, could provide an efficient solution for water quality prediction in aquaculture. |
| Author | Xiang, Tianqi Chi, Junjie Gao, Juan Guo, Xiangyun Zhang, Luwei |
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| CorporateAuthor | 1. College of Computer Science, Beijing Information Science & Technology University, Beijing 102206, China 2. College of Management Science and Engineering, Beijing Information Science & Technology University, Beijing 102206, China 3. College of Engineering, China Agricultural University, Beijing 100083, China |
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| DOI | 10.25165/j.ijabe.20251801.9074 |
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| SubjectTerms | Aquaculture Artificial neural networks Deep learning Energy consumption Machine learning Neural networks Organisms Prediction models Spatiotemporal data Time series Variables Water quality |
| Title | Enhancing aquaculture water quality forecasting using novel adaptive multi-channel spatial-temporal graph convolutional network |
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