【摘要】 在朴素贝叶斯分类的基础上建立了一种增强型分类器系统,并在对1997~2002年夏季青藏高原上MCS(Mesoscale Convective System)进行自动追踪的基础上,对MCS的移动方向与其周边环境物理量场的分布特征进行了分类研究.进而,将分类结果与决策树、人工神经网络分类方法进行了比较.研究表明,与其他分类方法相比,使用增强型的贝叶斯分类器预测MCS的移动路径具有较好的效果,这为揭示高原上MCS的移动规律、提高长江中下游地区灾害天气预报的准确率提供了一种有效的方法.更多还原

【Abstract】 In this paper,a Boosting Classifier based on Naive Bayesian Classification was built and applied to classify the trajectories of MCS,using a dataset of environmental physical field values around MCS,based on the automated tracking of MCS over the Tibetan Plateau in summer from 1997 to 2000.Furthermore,results comparing several classification methods found the Boosting Bayesian Classifier to be comparable in performance with decision tree and neural network classifiers in the application of prediction of the trajectories of MCS.So it is proven to be an effective method to reveal the trajectories of MCS over the Tibetan Plateau and improve the accuracy of forecasting the disaster weather in Yangtze River Basin.更多还原