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- W4214706788 abstract "By using of Gated Recurrent Unit and Graph Convolution network model, this work, based on remote sensing water quality measurement data, forecast the water quality which observed by the index of the chlorophyll A value and total nitrogen at the chosen observation points. The data is decomposed and reconstructed in time series in the light of the spatio-temporal correlation. The dependence of the processed sample is obviously decreased. The prediction results reflect the temporal and spatial changes of water quality data, and have faster processing speed and stronger computing power than other models." @default.
- W4214706788 created "2022-03-02" @default.
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- W4214706788 date "2022-02-01" @default.
- W4214706788 modified "2023-09-27" @default.
- W4214706788 title "Research on Application of Graph Neural Network in Water Quality Prediction" @default.
- W4214706788 doi "https://doi.org/10.1142/s021821302250018x" @default.
- W4214706788 hasPublicationYear "2022" @default.
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