Research on Wireless Microwave Rainfall Inversion Based on Composite Machine Learning Models
In order to realize the refined wireless microwave hydrometeorological intensive monitoring,a rainfall enhancement inversion scheme based on wireless microwave links was proposed.The eXtreme Gradient Boosting(XGBoost)algorithm was used to discriminate between dry and wet periods.It combined two machine learning algorithms,namely support vector regression(SVR)and Gaussian process regression(GPR),to construct a composite inversion model of XG_SVR and XG_GPR,then compared the inversion results with those of traditional ITU-R model.The results show that the discrimination of the XGBoost algorithm is better than those of the sliding standard deviation method,and the average classi-fication accuracy of XGBoost algorithm is 88％,the accuracy of high time resolution less than 1 h can reach more than 90％.The overall inversion results of the two composite models are good,both XG_SVR and ITU-R models are suitable for high time resolution,and the average correlation coefficient are higher than 0.80.The XG_GPR model has a significant advantage in retrieving rainfalls over of 1 h and above low time resolution rainfall,the correlation coefficient is about 0.95,and the mean square errors are much smaller than the other two models.Using composite ma-chine learning model to improve the traditional wireless microwave rainfall measurement scheme has feasibility and development potential.
microwave linkrainfallrain attenuation relationshipmachine learningclassification of dry and wet period
万方数据
维普
