# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 4 Conclusions  
Compared to earlier research on the quality control of radar data, the deep learning approach employed in our study primarily focuses on generated vector sea-current charts rather than radial velocities. This allows for a more effective integration with previous quality control efforts on radial velocities. Not only can we address anomalies resulting from synthetic algorithms, but our study also helps to rectify anomalies that persisted due to incomplete or suboptimal previous quality control. Moreover, our method does not rely on radar echo-related signals for quality assessment. Instead, it capitalizes on the continuity of time series and the strengths of deep learning, significantly simplifying the quality control process and improving the data quality.  
Figure 10: (**a,b**) illustrate the variations in data detection volume and the amount of undetected anomalous data under different input sequence lengths, and (**c**) depicts the changes in anomaly detection performance under varying input sequence lengths.  
In Section 2, we provided a comprehensive introduction to the Bi-LSTM neural network model, clarified the data preprocessing process, explained our reasons for choosing specific anomaly detection metrics, and detailed the architecture of the anomaly detection system. In Section 3, we began by discussing the various types of anomalies commonly found in radar sea current data and their respective distribution percentages. Subsequently, we optimized the model's input and detection process specifically for different anomaly types. In the concluding section of Section 3, we conducted a sensitivity analysis experiment designed to elucidate the effects of input sequence length variations on model performance.  
In our study, the quantitative results, as detailed in Figure 9,e, display the final PR curve of the model and the F-score, which stands as the core evaluation metric in our anomaly detection research. For datasets like high-frequency radar sea-current data, imbalances are prevalent where anomalies (or 'positive instances') are significantly outnumbered by normal observations. Such imbalanced datasets, characterized by large volumes of data and a high complexity, can pose challenges to conventional machine learning algorithms. In this context, our proposed method achieved an F-score of 0.814, signifying its strong performance in both precision and recall, and thus effectively tackling the anomalies.  
Our method has provided substantial assistance for the quality control of radar and offered insights into potential areas of exploration. However, there remains potential for further optimization in certain areas. In subsequent research, we aim to enhance the model's sensitivity to specific anomaly patterns and consider increasing the model's complexity to capture more information.  
Conceptualization, J.W.; Data curation, F.Y.; Formal analysis, C.Z.; Funding acquisition, J.W.; Investigation, C.Z.; Methodology, C.Z.; Resources, F.Y.; Supervision, C.W.; Validation, C.Z.; Visualization, C.Z.; Writing--original draft, C.Z.; Writing--review& editing, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Basic Research and Development Project of China (2022 YFF0802000), the project supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) No. SML2020 SP009 and the National Natural Science Foundation of China (41976007).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that parts of radar data on oceanic state information is classified.  
The authors declare no conflict of interest.