Application of Data Cubes for Improving Detection of Water Cycle Extreme Events
Arif R. Albayrak, William L. Teng · AGU Fall Meeting Abstracts · 2015
As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA (i.e., accessing archived time-step array as point-time series), for the hydrology and other point-time series-oriented communities, data are created from which time series files (aka data rods) are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A cube is a specific case of the general optimal strategy of reorganizing to match the desired means of access. The gain from such reorganization is greater the larger the set. As a use case of our project, we are leveraging existing software to explore the application of the cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using from the Tropical Rainfall Measuring Mission (TRMM).