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    • Title
  • Smart Aquaculture
    • Animal Re-identification
      • An Intelligence Cattle Reidentification System Over Transport by Siamese Neural Networks and YOLO
      • Convolutional Neural Networks for individual identification in the Southern Rock Lobster supply chai
      • Few-Shot Learning for Palmprint Recognition via Meta-Siamese Network
      • Re-identification of fish individuals of undulate skate via deep learning within a few-shot context
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      • Adapting Grad-CAM for Embedding Networks
      • CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identificationwithout Concrete Text Labels
      • FastReID: A Pytorch Toolbox for General InstanceRe-identification
      • GW-net: An efficient grad-CAM consistency neural network with weakening of random erasing features f
      • Style Normalization and Restitution for Generalizable Person Re-Identification
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      • Hyperspectral imaging–based assessment of fresh meat quality: Progress and applications
    • Sensor Data
      • A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anom
      • Big Data Driven Marine Environment Information Forecasting: A Time Series Prediction Network
      • Free nitrous acid prediction in ANAMMOX process using hybrid deep neural network model
      • Predicting ammonia nitrogen in surface water by a new attention-based deep learning hybrid model
      • Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)
      • Real-time in situ prediction of ocean currents
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  1. Smart Aquaculture
  2. Sensor Data

Real-time in situ prediction of ocean currents

PreviousPrediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)

Last updated 1 year ago

Journal: Ocean Engineering (Q1)

Abstract

Deep autoregressive networks (LSTM and Transformer) can predict ocean currents in real-time at any location in the world.

Data

Ocean current data at 831 sites (222 stations have data length greater than 2 months) from . Sampling interval: 6 or 10 min

Conclusion

LSTM and Transformer can predict ocean currents in real-time at any location in the world and even better than traditional Harmonic Method.

e.g., Comparison of speed Normalized Root Mean Squared Error (NRMSE) (lower is better)

Station ID
Depth (ft)
LSTM
TF
HM

hb0401

15.7

0.14

0.12

0.12

jx0302

29.5

0.1

0.09

0.33

jx0701

15

0.09

0.10

0.12

cb0701

13.9

0.18

0.19

0.2

NOAA