# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 1 Introduction  
Hainan Island, nestled in the northern part of the South China Sea, enjoys a warm tropical climate. This climate bestows upon the island a rich biodiversity and stunning natural landscapes. Economically speaking, the island stands out as a hotspot for both tourism and fishing. However, the tropical climate brings its own set of challenges, including seasonal storms and the long-term effects of climate change. Given Hainan's unique status in China and the various challenges it faces, gaining insights regarding and monitoring its marine currents is crucial for ensuring the ecological and economic stability of Hainan and its surrounding maritime regions. High-Frequency Radar (HFR) has emerged as a revolutionary tool in marine observation in recent years. By harnessing high-frequency electromagnetic waves, it not only captures subtle changes on the ocean's surface but also, through cutting-edge inversion techniques, accurately deciphers activities of winds, waves, and crucial ocean currents [1; 2; 3; 4]. The application of this technology offers researchers extensive, real-time marine data, and provides essential informational support and solutions in the realms of global climate change and marine safety. HFR technology has demonstrated high accuracy in measuring winds, waves, and currents on the ocean surface. This achievement has been widely recognized by the academic community [5; 6; 7; 8; 9; 10]. However, like other measuring tools, HFR faces inherent challenges in its measurements. These include inaccuracies due to equipment errors, electromagnetic interference, or data loss during transmission [11; 12]. In previous studies, the quality control efforts for HFR were mainly centered on radial velocity data. These data reveals the speed component of the ocean's surface from the radar to a specific point. To obtain a complete ocean current vector from this data, radial velocity information is needed from at least two different radar locations. These data are then combined to produce a two-dimensional vector, representing the flow direction and speed of the ocean's surface at a specific point. To form a continuous ocean current vector field, researchers typically employ mathematical methods to merge radial velocity data from multiple radar stations onto a grid. One commonly used method relies on inverse interpolation techniques, utilizing the distance and angular differences between radar stations to estimate velocity values for each grid point. In addition, there is a method based on the least squares principle that optimally fits data gathered from different radar stations by optimizing the model. Despite utilizing these advanced mathematical approaches, the merging process can still produce anomalous vector data. These anomalies can stem from mismatches between radar data, limitations of the merging algorithm, the incomplete or subpar quality control of radial velocities, and other factors. Issues with the quality of radial velocities, such as noise, discontinuity, or other unstable elements, might be partially addressed in the initial quality control phase but can be magnified or lead to new anomalies during the merging process. In terms of radar data quality control, researchers have adopted various methods. Traditional approaches mainly focus on the quality control of radial velocity data obtained from high-frequency radars. The accuracy of these data largely hinges on the quality of electromagnetic signals and inherent characteristics of radial velocities. Past research has concentrated on the following areas:  
Signal-to-noise ratio control: [PERSON] [13] and colleagues advocated for using the signal-to-noise ratio as a pivotal quality metric. They discovered that, when the ratio exceeds a certain threshold, the data accuracy notably improves. Based on this, they devised an algorithm to filter and correct radar data according to the signal-to-noise ratio. Spatial analysis: [PERSON] [14] and his team proposed a quality control method based on spatial characteristics.