Weather Radar image Based Forecasting using Joint Series Prediction

<p>Accurate rainfall forecasting using weather radar imagery has always been a crucial and predominant task in the field of meteorology [1], [2], [3] and [4]. Competitive Radial Basis Function Neural Networks (CRBFNN) [5] is one of the methods used for weather radar image based forecasting. Recently, an alternative CRBFNN based approach [6] was introduced to model the precipitation events. The difference between the techniques presented in [5] and [6] is in the approach used to model the rainfall image. Overall, it was shown that the modified CRBFNN approach [6] is more computationally efficient compared to the CRBFNN approach [5]. However, both techniques [5] and [6] share the same prediction stage. In this thesis, a different GRBFNN approach is presented for forecasting Gaussian envelope parameters. The proposed method investigates the concept of parameter dependency among Gaussian envelopes. Experimental results are also presented to illustrate the advantage of parameters prediction over the independent series prediction.</p>

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering

Year

2010

Author dc:creator

• Kattekola, Sravanthi

Contributors dc:contributor

• Charalampidis, Dimitrios
• Bourgeois, Edit
• Jovanovich, Kim

• Radial Basis Function Neural Network
• Forecasting
• Time Series Prediction

Repository record dc:identifier

https://scholarworks.uno.edu/td/1238

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-2221