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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Veröffentlicht in:	International journal of agricultural and biological engineering Jg. 18; H. 1; S. 279 - 291
Hauptverfasser:	Xiang, Tianqi, Guo, Xiangyun, Chi, Junjie, Gao, Juan, Zhang, Luwei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Beijing International Journal of Agricultural and Biological Engineering (IJABE) 01.02.2025
Schlagworte:	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-Zugang:	Volltext
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