SEA-ViT: Sea Surface Currents Forecasting Using Vision Transformer and GRU-Based Spatio-Temporal Covariance Modeling

• URL: http://arxiv.org/abs/2409.16313v2
• Date: Thu, 26 Sep 2024 01:26:56 GMT
• Title: SEA-ViT: Sea Surface Currents Forecasting Using Vision Transformer and GRU-Based Spatio-Temporal Covariance Modeling
• Authors: Teerapong Panboonyuen,
• Abstract summary: This paper introduces SEA-ViT, an advanced deep learning model that integrates Vision Transformer with Gate Recurrent Units. SEA-ViT is designed to unravel complex dependencies by leveraging a rich dataset spanning over 30 years.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting sea surface currents is essential for applications such as maritime navigation, environmental monitoring, and climate analysis, particularly in regions like the Gulf of Thailand and the Andaman Sea. This paper introduces SEA-ViT, an advanced deep learning model that integrates Vision Transformer (ViT) with bidirectional Gated Recurrent Units (GRUs) to capture spatio-temporal covariance for predicting sea surface currents (U, V) using high-frequency radar (HF) data. The name SEA-ViT is derived from ``Sea Surface Currents Forecasting using Vision Transformer,'' highlighting the model's emphasis on ocean dynamics and its use of the ViT architecture to enhance forecasting capabilities. SEA-ViT is designed to unravel complex dependencies by leveraging a rich dataset spanning over 30 years and incorporating ENSO indices (El Ni\~no, La Ni\~na, and neutral phases) to address the intricate relationship between geographic coordinates and climatic variations. This development enhances the predictive capabilities for sea surface currents, supporting the efforts of the Geo-Informatics and Space Technology Development Agency (GISTDA) in Thailand's maritime regions. The code and pretrained models are available at \url{https://github.com/kaopanboonyuen/gistda-ai-sea-surface-currents}.

Related papers

• Regional Ocean Forecasting with Hierarchical Graph Neural Networks [1.4146420810689422]
  We introduce SeaCast, a neural network designed for high-resolution, medium-range ocean forecasting. SeaCast employs a graph-based framework to handle the complex geometry of ocean grids and integrates external forcing data tailored to the regional ocean context. Our approach is validated through experiments at a high spatial resolution using the operational numerical model of the Mediterranean Sea provided by the Copernicus Marine Service.
  arXiv  Detail & Related papers  (2024-10-15T17:34:50Z)
• Outlier detection in maritime environments using AIS data and deep recurrent architectures [5.399126243770847]
  We present a methodology based on deep recurrent models for maritime surveillance, over publicly available Automatic Identification System (AIS) data. The setup employs a deep Recurrent Neural Network (RNN)-based model, for encoding and reconstructing the observed ships' motion patterns. Our approach is based on a thresholding mechanism, over the calculated errors between observed and reconstructed motion patterns.
  arXiv  Detail & Related papers  (2024-06-14T12:15:15Z)
• A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance [59.21686775951903]
  Various animals exhibit accurate navigation using environment cues. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps.
  arXiv  Detail & Related papers  (2024-02-06T13:20:56Z)
• Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble [0.3626013617212667]
  We propose an adaptive surrogate modeling approach named LANE-SI. It uses ensemble of relatively simple deep learning models with different loss functions for forecasting of sea ice concentration in the specified water area. We achieve a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.
  arXiv  Detail & Related papers  (2023-12-07T14:48:30Z)
• Multi-decadal Sea Level Prediction using Neural Networks and Spectral Clustering on Climate Model Large Ensembles and Satellite Altimeter Data [0.0]
  We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region.
  arXiv  Detail & Related papers  (2023-10-06T19:06:43Z)
• Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting [52.77986479871782]
  Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts. In this work, we investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin and improve its quality by using additional weather data and training on multiple regions.
  arXiv  Detail & Related papers  (2022-10-17T09:14:35Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks [77.34726150561087]
  We propose to consider the use of convolution neural networks in task of air shower characteristics determination. We use CNN to analyze HiSCORE events, treating them like images. In addition, we present some preliminary results on the determination of the parameters of air showers.
  arXiv  Detail & Related papers  (2021-12-19T15:18:56Z)
• SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model [72.3183990520267]
  We propose SALT: Sea lice Adaptive Lattice Tracking approach for efficient estimation of sea lice dispersion and distribution. Specifically, an adaptive spatial mesh is generated by merging nodes in the lattice graph of the Ocean Model based on local ocean properties. The proposed SALT technique shows promise for enhancing proactive aquaculture management through predictive modelling of sea lice infestation pressure maps in a changing climate.
  arXiv  Detail & Related papers  (2021-06-24T17:29:42Z)
• Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks [9.541153192112194]
  We propose the use of convolutional neural networks (ConvNets) to estimate fields free of internal tide signals. We also investigate the relevance of considering additional data from other sea surface variables such as sea surface temperature (SST)
  arXiv  Detail & Related papers  (2020-05-03T14:02:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.