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Precipitation data fusion using vector space transformation and artificial neural networks

Authors
Journal
Pattern Recognition Letters
0167-8655
Publisher
Elsevier
Publication Date
Volume
31
Issue
10
Identifiers
DOI: 10.1016/j.patrec.2009.12.033
Keywords
  • Pattern Recognition
  • Optimization
  • Data Merging
  • Convergence
  • Artificial Neural Networks
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract We have developed a new methodology to fuse several precipitation datasets, available from different estimation techniques. The method is based on artificial neural networks and vector space transformation function. The final merged product is statistically superior to any of the individual datasets over a seasonal period. The results have been tested against ground-based measurements of rainfall over a study area. This method is shown to have average success rates of 85% in the summer, 68% in the fall, 77% in the spring, and 55% in the winter.

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