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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Bibliographic Details
Published in:International journal of agricultural and biological engineering Vol. 18; no. 1; pp. 279 - 291
Main Authors: Xiang, Tianqi, Guo, Xiangyun, Chi, Junjie, Gao, Juan, Zhang, Luwei
Format: Journal Article
Language:English
Published: Beijing International Journal of Agricultural and Biological Engineering (IJABE) 01.02.2025
Subjects:
Aquaculture
Artificial neural networks
Deep learning
Energy consumption
Machine learning
Neural networks
Organisms
Prediction models
Spatiotemporal data
Time series
Variables
Water quality
China
Laizhou Bay
ISSN:1934-6344, 1934-6352
Online Access:Get full text
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Summary: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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ISSN:1934-6344
1934-6352
DOI:10.25165/j.ijabe.20251801.9074