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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Vydané v:	International journal of agricultural and biological engineering Ročník 18; číslo 1; s. 279 - 291
Hlavní autori:	Xiang, Tianqi, Guo, Xiangyun, Chi, Junjie, Gao, Juan, Zhang, Luwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Beijing International Journal of Agricultural and Biological Engineering (IJABE) 01.02.2025
Predmet:	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
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