Identifying chaff echoes in weather radar data using tree-initialized fuzzy rule-based classifier
Identifying chaff echoes in weather radar data using tree-initialized fuzzy rule-based classifier
复制标题
使用树初始化模糊基于规则的分类器识别气象雷达数据中的箔条回波
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Sungshin Kim
中科院分区：
文献类型：
--
作者：
Jungwon Yu;Hansoo Lee;Yeongsang Jeong;Sungshin Kim
为了产生可靠的天气预报，必须区分天气雷达数据中的非气象目标和雨云。作为主要噪声源之一的箔条回波的识别，对于熟练的气象专家来说是不确定和不精确的，因为它们的特征与降水回波的特征相似。采用树形初始化的模糊分类器(FC)对箔条回波进行识别。模糊模型已被广泛应用于不确定性和模糊性领域。分类回归树用于生成初始CRISP模型(一组CRISH规则)。由性能准则系统地确定与模型复杂性相对应的规则数目。最后，在将CRISP模型直接转换为模糊模型后，利用遗传算法对FCS的参数进行优化。FCS比矩形划分的二叉决策树具有更灵活的决策边界。为了评价识别性能，将FCS和比较法应用于多种同时出现箔条和非箔条回波的情况。实验结果表明，FCS具有最好的辨识性能。
In order to produce reliable weather forecasts, it is essential to discriminate non-meteorological targets from rain clouds in weather radar data. Identification of chaff echoes, which is one of the main noise sources, is uncertain and imprecise for skilled weather experts because characteristics of them are similar to those of precipitation echoes. This paper uses tree-initialized fuzzy classifier (FC) to identify chaff echoes. Fuzzy models have been widely applied to the domain of uncertainty and vagueness. Classification and regression tree is used to generate an initial crisp model (a set of crisp rules). The number of the rules, corresponding to complexity of the model, is systematically determined by performance criterion. Finally, after transforming the crisp model to the fuzzy one straightforwardly, parameters of the FCs are optimized by genetic algorithms. FCs have more flexible decision boundaries than binary decision trees with rectangular partitioning. In order to evaluate identification performance, the FCs, and comparison methods are applied to many cases where both chaff and non-chaff echoes occurred simultaneously. The results of experiments show that the FCs achieve the best identification performance.