Machine Learning Classification of Rainfall Types Based on the Differential Attenuation of Multiple Frequency Microwave Links
| Autor: | Xichuan Liu, Taichang Gao, Xian Minghao, Kang Pu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Noise (signal processing)
business.industry Gaussian Attenuation Decision tree Pattern recognition Linear discriminant analysis law.invention symbols.namesake Statistical classification Probabilistic neural network law symbols General Earth and Planetary Sciences Artificial intelligence Electrical and Electronic Engineering Radar business Mathematics |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 58:6888-6899 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2020.2977393 |
| Popis: | This article proposes a new rainfall-type classification method using the differential attenuation rates (DARs) calculated from microwave links with multiple frequencies and dual polarization. Several machine-learning algorithms were employed for classification [decision tree (DT), probabilistic neural network (PNN), Gaussian discriminant analysis (GDA), and logistic regression (LR)]. This method was simulated by two or three frequencies (combination of frequencies of 15, 25, 38, 60, and 80 GHz). The results showed that DT, PNN, GDA, and LR achieved maximum accuracies of 88.9%, 84.6%, 66.8%, and 67.6% for the dual-frequency model, respectively, and 89.6%, 85.8%, 67.6%, and 68.7% for the tri-frequency model, respectively, which is obviously better than the classification method based on the sequence of rain rates from the ITU-R model. The model accuracies based on GDA and LR algorithms are obviously lower than those based on DT and PNN due to the defects of the algorithms. In addition, the effects of different noise levels on classification performance are also discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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