Oil Spill Drift Prediction

Oil Spill Drift Prediction Enhanced by Correcting Numerically Forecasted Sea Surface Dynamic Fields With Adversarial Temporal Convolutional Networks featured image
Adversarial Temporal Convolutional Networks(ATCNs)

Oil Spill Drift Prediction Enhanced by Correcting Numerically Forecasted Sea Surface Dynamic Fields With Adversarial Temporal Convolutional Networks

Timely and accurate representation of sea surface dynamic fields is crucial for oil spill drift prediction. Numerically forecasted sea surface dynamic fields are available in a …

Peng Ren
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An adversarial learning approach to forecasted wind field correction with an application to oil spill drift prediction featured image
Adversarial Learning

An adversarial learning approach to forecasted wind field correction with an application to oil spill drift prediction

Reanalysis wind fields are obtained by correcting the numerically forecasted wind fields based on observation data (i.e., either remote sensing or in-situ observations, or both). …

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李永庆
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