A data warehousing and data mining approach for analysis and forecast of cloudburst events using OLAP-based data hypercube

Kavita Pabreja and Rattan K. Datta

International Journal of Data Analysis Techniques and Strategies, 2012, vol. 4, issue 1, 57-82

Abstract: The multidimensional data model can be effectively utilised for analysing huge and detailed meteorological datasets forecasted by numerical weather prediction (NWP) model. The model cannot predict any weather event directly. The output products of model are interpreted by man-machine mix to infer the idiosyncratic behaviour of weather events. The mathematical tools for analysis and forecasting are able to provide forecast of weather variables only at grid-points. In this paper, the technology of dimension modelling has been adapted for analysing NWP model output datasets corresponding to sub-grid scale events viz. cloudburst, using OLAP technique. The huge datasets of weather variables available directly and derived indirectly, are mined so as to locate the patterns of cloudburst formation. K-means clustering technique has been used to generate clusters of convergence and divergence, for four real-life cases of cloudburst. It has been observed that clustering technique can help in identification of patterns conducive to formation of cloudburst.

Keywords: OLAP; online analytical processing; cloudbursts; numerical weather prediction; k-means clustering; cluster analysis; convergence; data mining; data warehouses; dimension modelling; data hypercubes; forecasts; forecasting; multidimensional data models; meteorological datasets; weather events; meteorology; output products; man-machine mix; idiosyncratic behaviour; weather variables; grid-points; sub-grid scale events; output datasets; cloudburst formations; formation patterns; clouds; divergence; Dhaka; Bangladesh; Pittorgarh; Uttarakhand; Chamoli; Shimla; Himachal Pradesh; India; data analysis techniques; data analysis strategies. (search for similar items in EconPapers)
Date: 2012
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