A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks

We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach em...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 21; p. 11826
Main Authors Zhou, Chunye, Wei, Chunlei, Yang, Fan, Wei, Jun
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2023
Subjects
Accuracy
anomaly detection
Bi-LSTM
Climate change
Data collection
Energy consumption
F-score
Global temperature changes
HF radar
Human error
Information management
Machine learning
Methods
Missing data
Neural networks
Ocean currents
Quality control
Quality standards
Radar systems
recall
Signal to noise ratio
South China Sea
Storms
Time series
Velocity
South China Sea
Beijing China
China
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Summary:We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach employs a Bi-LSTM neural network model. This method aims to improve data quality and streamline the quality control process. Through a series of analyses, we demonstrate the feasibility of using machine learning techniques to enhance radar data quality.
ISSN:2076-3417
2076-3417
DOI:10.3390/app132111826