Precipitation data fusion using vector space transformation and artificial neural networks

被引：12
作者：
Turlapaty, Anish C. [1 ,2 ]
Anantharaj, Valentine G. [2 ]
Younan, Nicolas H. [1 ,2 ]
Turk, F. Joseph [3 ]
机构：
[1] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA
[2] Mississippi State Univ, Geosyst Res Inst, Mississippi State, MS 39762 USA
[3] USN, Res Lab, Marine Meteorol Div, Monterey, CA 93943 USA
来源：
PATTERN RECOGNITION LETTERS | 2010年 / 31卷 / 10期
关键词：
Pattern recognition; Optimization; Data merging; Convergence; Artificial neural networks; PASSIVE MICROWAVE; ALGORITHM;
D O I：
10.1016/j.patrec.2009.12.033
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码：1184 / 1200
页数：17
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