# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 3 Results
### Continuous Anomalies  
Continuous anomalies represent another focal area of our study. Compared to single-point anomalies, these anomalies present a greater detection challenge due to their persistent nature in time series data. As shown in Figure 8a,b, when the predictive model encounters continuous anomalies, it struggles to generate predictions that significantly deviate from the anomalous data. This observation can be explained in two ways. Firstly, while the Bi-LSTM model excels at capturing long-term dependencies in time series data, when faced with consecutive anomalous data points, the model may over-rely on previous \"memories\" to interpret the current data point, leading to reduced sensitivity to continuous anomalies. Secondly, although the bidirectional nature of Bi-LSTM allows it to capture both past and future context in an input sequence, this mechanism may lack sufficient discriminative power when faced with a large number of consecutive anomalies. Figure 8c illustrates the comparison of the recall rates for continuous and single-point anomalies at different thresholds. As the threshold increases, the recall rate for single-point anomalies decreases slowly, while that for continuous anomalies drops rapidly. Combining  
Figure 7: **(a,b) Comparison of predictions before and after model improvement, with the orange curve representing the predicted model data and the blue curve representing the original radar data. (c) PR (precision–recall) curve before and after model improvement, (d) recall and precision graph as threshold varies after improvement. **  
the specific examples from Figure 8a,b, it becomes clear that the model, when predicting certain continuous anomalies, cannot generate sufficiently large residuals to distinguish them from normal data. Figure 8d shows that, even when the model's F-score is at its peak, the recall rate for continuous anomalies remains significantly lower than that for single-point anomalies. The findings from all sub-figures in Figure 8 emphasize the challenges that continuous anomalies pose to the predictive model. In summary, while the model's performance in detecting single-point anomalies is commendable, there is room for optimization in the detection of continuous anomalies. Thus, the next steps in research should focus on further enhancing the model's performance in this area. This iterative process presents a practical solution for addressing continuous anomalies, effectively transforming them into a sequence of single-point anomaly detections. This approach streamlines the detection process and has the potential to enhance the model's performance in handling continuous anomalies. Figure 8: (**a,b**) Comparison between model predicted output (orange curve) and radar data (blue curve) for continuous anomalies, (**c**) illustration of recall for different anomaly types as threshold varies, (**d**) bar chart of recall for various anomaly types when model achieves maximum F-score. In order to ensure rationality, during the iterative forecasting process, the data being replaced should ideally consist of as many abnormal data points as possible. As a result, the selected threshold should guarantee a high level of accuracy. In this study, a threshold corresponding to an accuracy of 90% or higher was used. As shown in Figure 7d, the minimum threshold value is 0.3 m/s. The threshold values range from 0.3 to 0.5 m/s in increments of 0.01 m/s, and the number of iterations ranges from one to five times. The specific results are illustrated in Figure 9.  
Figure 9: (**a**) F-score graphs under different thresholds, with different line colors indicating iteration counts. (**b**,**c**) Comparison graphs of predictions before and after model improvement, with the orange curve representing the predicted model data and the blue curve representing the radar data. (**d**) PR (precision–recall) curve graphs before and after model improvement, (**e**) bar chart comparing recall rates for different anomaly types before and after model improvement.