Date of Award
12-2009
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Engineering
Department
Electrical Engineering
Major Professor
Charalampidis, Dimitrios
Second Advisor
Jilkov, Vesselin
Third Advisor
Chen, Huimin
Abstract
Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed.
Recommended Citation
Paduru, Anirudh, "Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery" (2009). University of New Orleans Theses and Dissertations. 1097.
https://scholarworks.uno.edu/td/1097
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.