ITR: A Real Time Mining of Integrated Weather Data

Theodore B. Trafalis俄克拉何马大学ITR：综合天气数据的实时挖掘任务是建立系统和发展理论，用于提取信息和识别对实时决策有用的模式。正在申请资金，以建立模式识别技术，以综合方式利用多传感器数据，提供诸如龙卷风、超级单体和中气旋存在与否等信息；估计降水;预测山洪暴发的发生；吸收和显示大量的多传感器数据，并触发选定数据集的存档。这些任务将通过定制和开发实时数据挖掘技术来完成。使用的方法将包括传统的数据简化方法，如PCA和聚类；普罗克汝斯忒斯分析;卡尔曼滤波与状态切换非线性时间序列分析研究了训练支持向量机的分解和鲁棒优化方法。

EIA-0205628 Theodore B. Trafalis University of Oklahoma ITR: A Real Time Mining of Integrated Weather DataThe mission is to build systems and develop theory for extracting information and identifying patterns that are useful for making decision in real-time. Funding is being requested to build pattern recognition techniques that will exploit multisensor data in an integrated manner to provide information such as the presence or absence of tornados, supercells and mesocyclones; estimate precipitation; predict the occurrence of flash floods; assimilate and display large volumes of multisensor data and trigger the archive of selected data sets. These tasks will be accomplished by customizing and developing techniques for real-time data mining. The approaches used will include traditional data reduction methods such as PCA and clustering; Procrustes analysis; Kalman filters and non-linear time series analysis with regime switching; and, decomposition and robust optimization methods for training support vector machines.