This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various ...
Many application areas have faced significant challenges as a result of missing data. For example, in weather and traffic, missing data from the data gathering process due to sensor failure or network ...
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