Study On Hydrometeor Classification Method Of Dual-Polarization Radar Based On BNT

As a new type of detectable weather tool,dual polarization meteorological radar can emit and receive electromagnetic waves in horizontal and vertical polarization directions alternately as well as process the echo data and extract information of the shape,size,spatial orientation and different precipitation particles.Dual polarization meteorological radar has obvious advantages in precipitation measurement,hydrometeor classification method and recognition,and severe weather forecast compared with traditional meteorological radar.Most of the dual polarization meteorological radars use fuzzy logic as hydrometeor classification method in current.However,a big weak point of fuzzy logic is that a number of parameters which need to be determined by expert experience.At the same time,a single membership function or probability model is used to describe the distribution of hydrometeor in hydrometeor classification method currently.But a preset single model will have a greater impact on results of hydrometeor classification method.Bayesian network which can obtain knowledge from samples as well as no need preset model is one of the most effective theoretical models in the field of uncertain knowledge expression and reasoning.It has been successfully applied to machine learning,artificial intelligence,medical diagnosis,big data Analysis and many other fields.Therefore,it is significant to use Bayesian networks in hydrometeor classification method of dual polarization meteorological radar.The main contents of this article are as follows:The problems that the traditional hydrometeor classification method relies on expert experience too much and the single hypothetical model will lead to errors exist,therefore a dual polarization meteorological radar precipitation particle classification algorithm based on discrete attribute Bayesian network is proposed.First,the radar polarization parameters are discretized,and the training data set is constructed based on this;then the training data is used to learn Bayesian network structure,parameter learning,and determine the class prior probability in order to construct a Bayesian network classifier;finally the trained Bayesian network classifier is used to implement the classification of hydrometeor according to the maximum posterior probability criterion.BNT directly achieves knowledge from the training data to complete the classification of hydrometeor without the need for model presetting.Experiment shows that the generalization,robustness and operability of the classifier have been improved.In view of the fact that labeled training data sets are difficult to obtain,but a large number of unlabeled training data is easy to obtain,a dual polarization meteorological radar hydrometeor classification method based on BNT-DTSVMs collaborative training is proposed.Experiment shows that the BNT-DTSVMs algorithm completes the classification of hydrometeor in a small way when using a part of the labeled and part of the unlabeled training data,and obtains a good result of hydrometeor classification.