To improve the accuracy of rainfall estimation by microwave links, this article presents a method for classifying wet and dry periods based on the support vector machine (SVM). The average, minimum, and maximum attenuation measurements in 5 min are applied as the feature vector of the SVM after the analysis of the relation between the statistical parameters of the attenuation measurements from seven microwave links and the wet/dry periods. When the baseline attenuation is needed for retrieving the path-averaged rain rate, the method can classify the wet/dry periods and estimate a dynamic baseline with an optimal combination of the statistical parameters of the attenuation measurements based on the prior training. Experiments are conducted to test the classification method. The results show that the classification accuracy is higher than 0.8, which is a satisfactory result. Most values of the true positive rate are higher than 0.9, which indicates that the method can correctly classify most of the wet periods. Additionally, the values of the false positive rate are less than 0.3, and most of the values are less than 0.2, suggesting that the method incorrectly classifies the dry period as the wet period with a low probability. The results demonstrate that the classification method is capable of classifying the wet and dry periods with a high accuracy, which can help improve the precision of the baseline of microwave links and rainfall estimation.

中文翻译：

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利用微波链路检测降雨的实验研究：干湿期的分类

为了提高微波链路降雨估计的准确性，本文提出了一种基于支持向量机（SVM）的干湿期分类方法。在分析了来自七个微波链路的衰减测量的统计参数与湿/干时段之间的关系后，将 5 分钟内的平均、最小和最大衰减测量值用作 SVM 的特征向量。当需要基线衰减来检索路径平均降雨率时，该方法可以对湿/干时段进行分类，并基于先前的训练利用衰减测量的统计参数的最佳组合来估计动态基线。进行实验以测试分类方法。结果表明分类准确率高于0.8，这是一个令人满意的结果。大部分真阳性率值都高于0.9，表明该方法可以正确分类大部分丰水期。此外，假阳性率的值小于0.3，大部分值小于0.2，表明该方法错误地将干旱期归类为湿润期的概率很低。结果表明，该分类方法能够对干湿期进行高精度分类，有助于提高微波链路基线和降雨量估算的精度。且大部分值均小于0.2，说明该方法将旱季误判为丰水期的概率较低。结果表明，该分类方法能够对干湿期进行高精度分类，有助于提高微波链路基线和降雨量估算的精度。且大部分值均小于0.2，说明该方法将旱季误判为丰水期的概率较低。结果表明，该分类方法能够对干湿期进行高精度分类，有助于提高微波链路基线和降雨量估算的精度。