Intelligent quality control method for marine buoy data based on transformer encoder and BiLSTM

Abstract

Ocean moored buoys are essential ocean monitoring devices that are permanently moored in the sea to collect real-time hydrological and meteorological data. In response to the anomalies and missing data in datasets collected from ocean moored buoys, this paper innovatively established an intelligent quality control Transformer-Encoder-BiLSTM model. This model can impute missing data and identify anomalies in buoy datasets. The model first uses the multi-head attention mechanism of the Transformer Encoder to extract global features from time-series data of buoy observations. Subsequently, it utilizes the BiLSTM network for temporal reasoning training to capture dynamic changes within the time series, predicted data. Finally, using the predicted data as a benchmark, the model conducts anomaly detection, fills in missing values, and rectifies stuck values. We conducted a series of comprehensive experiments, with the data from Buoy No. 0199 in Qingdao, China as an illustrative example. The experimental results indicate that the performance indicator R² of the model is above 0.9, the accuracy of quality control is above 97%, while both precision and recall are above 84%. The F1 scores range between 81.61% and 90.09%. These experiments demonstrate that this method exhibits high accuracy and efficiency in filling in missing data, rectifying stuck values and identifying anomalous data, showing broad application potential.