A spatio-temporal GNN model reduces storm surge water-level forecast RMSE by more than 70% for 48-hour horizons and over 50% for 72-hour horizons on U.S. Gulf Coast hurricane data.
Differential treatment for time and frequency dimensions in mel-spectrograms: An efficient 3d spectrogram network for underwater acoustic target classification
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A two-stage gradient boosted model with random effects predicts Arctic vessel movement probability (AUC 0.85) and conditional positive speed (77% out-of-fold variance explained), highlighting distance to coast and bathymetric depth as dominant factors.
MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.
citing papers explorer
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StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model
A spatio-temporal GNN model reduces storm surge water-level forecast RMSE by more than 70% for 48-hour horizons and over 50% for 72-hour horizons on U.S. Gulf Coast hurricane data.
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A Gradient Boosted Mixed-Model Machine Learning Framework for Vessel Speed in the U.S. Arctic
A two-stage gradient boosted model with random effects predicts Arctic vessel movement probability (AUC 0.85) and conditional positive speed (77% out-of-fold variance explained), highlighting distance to coast and bathymetric depth as dominant factors.
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A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition
MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.