# An intelligent method for Buoy meteorological data restoration using a Spatio-Temporal Dual-Attention Network with transformer and GAT

## TL;DR

This paper introduces a new deep learning model called ST-DAN to accurately restore missing or corrupted meteorological data from ocean buoys.

## Contribution

The novel Spatio-Temporal Dual-Attention Network (ST-DAN) combines Transformer and GAT to better capture spatio-temporal dependencies in buoy data.

## Key findings

- ST-DAN outperformed baseline models like ARIMA and Bi-LSTM in data restoration metrics.
- The model achieved high precision in interpolating and correcting anomalies in temperature and wind speed data.

## Abstract

Meteorological sensors deployed on ocean buoys frequently suffer from data loss or outliers due to electromagnetic interference and component failures caused by harsh weather and environmental conditions. Accurate reconstruction of corrupted buoy data remains a significant challenge, as conventional interpolation and imputation methods often fail to capture the inherent spatio-temporal dependencies in marine meteorological variables. To address this issue, this paper proposes a novel deep learning model that integrates Transformer and Graph Attention Network (GAT) architectures—termed the Spatio-Temporal Dual-Attention Network (ST-DAN). The model uses parallel computing to capture two aspects of the data: on one hand, it captures temporal dependencies through a Transformer enhanced by position encoding; on the other, it models inter-variable spatial correlations with a Graph Attention Network (GAT) based on a physically informed adjacency matrix, which dynamically adjusts the influence weights between variables to significantly enhance reconstruction accuracy. To evaluate the ST-DAN model, extensive experiments were conducted leveraging the ERA5 reanalysis dataset and in-situ observations from a Qingdao buoy, focusing on the reconstruction of temperature and wind speed data. The experiment result shows that ST-DAN outperformed baseline models (e.g., ARIMA, RNN, Bi-LSTM, and Transformer) across metrics including MAE, MSE, RMSE, and R². It indicates that the proposed model (ST-DAN) is off high robustness and achieves high-precision interpolation and anomaly correction for meteorological data.