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首页> 外文会议>Tenth International Conference on Modelling, Monitoring and Management of Air Pollution Jul, 2002 City of Segovia >Wind classification through cluster analysis for the development of predictive statistical models on atmospheric pollution
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Wind classification through cluster analysis for the development of predictive statistical models on atmospheric pollution

机译：通过聚类分析进行风分类，以建立大气污染的预测统计模型

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This paper presents the results obtained after the use of a two-stage clustering analysis to get a classification scheme of surface winds. For this purpose, we have studied data from four meteorological towers within the city of Cartagena, a Mediterranean city located at Southeast of Spain, with a complex topography for wind circulation. Semi-hourly data for wind parameter have been used through different hours of the day, representing the daily wind behaviour. These values, projected on a X-Y axis have been processed thorough a factor analysis in order to avoid colineality. After the factor scores were calculated from the factorial matrix, a two-step cluster analysis procedure, using both hierarchical (average linkage) and non-hierarchical techniques (k-means) was performed. This classification scheme identified five distinct wind flows, which were represented for each meteorological station using wind roses. An homogeneous behaviour for each cluster within each meteorological station was determined. Once different wind flows were identified for this area, total daily pollen concentrations were studied, associated to each cluster situation. In this sense, statistically significant differences were determined. This analysis will be further used to develop refined regression models of pollen concentrations, in order to get better results than those obtained with the whole data.
机译：本文介绍了使用两阶段聚类分析获得的表面风分类方案后获得的结果。为此，我们研究了来自卡塔赫纳市（位于西班牙东南部的地中海城市）中四座气象塔的数据，该市地处复杂的风环流。在一天的不同时段使用了风参数的半小时数据，代表每日的风行为。这些投影在X-Y轴上的值已通过因素分析进行​​了处理，以免产生共线性。从阶乘矩阵计算出因子得分后，执行了两步聚类分析程序，使用了层次（平均链接）和非层次技术（k均值）。该分类方案确定了五种不同的风流，分别使用风玫瑰代表了每个气象站。确定了每个气象站内每个群集的同质行为。一旦确定了该区域不同的风流，便会研究与每个集群情况相关的每日总花粉浓度。在这个意义上，确定了统计学上的显着差异。该分析将进一步用于开发花粉浓度的精细回归模型，以便获得比使用整个数据获得的结果更好的结果。

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