Learning networks in rainfall estimation

被引：6
|
作者：
Trafalis, Theodore B. [1 ]
Santosa, Budi [1 ]
Richman, Michael B. [2 ]
机构：
[1] Univ Oklahoma, Sch Ind Engn, 202 W Boyd,CEC 124, Norman, OK 73019 USA
[2] Univ Oklahoma, Sch Meteorol, Norman, OK 73019 USA
来源：
COMPUTATIONAL MANAGEMENT SCIENCE | 2005年 / 2卷 / 03期
关键词：
Artificial neural networks; support vector machines; kernel functions; rainfall estimation; radar;
D O I：
10.1007/s10287-005-0026-0
中图分类号：
O1 [数学]; C [社会科学总论];
学科分类号：
03 ; 0303 ; 0701 ; 070101 ;
摘要：
This paper utilizes Artificial Neural Networks (ANNs), standard Support Vector Regression (SVR), Least-Squares Support Vector Regression (LS-SVR), linear regression (LR) and a rain rate (RR) formula that meteorologists use, to estimate rainfall. A unique source of ground truth rainfall data is the Oklahoma Mesonet. With the advent of the WSR-88D network of radars data mining is feasible for this study. The reflectivity measurements from the radar are used as inputs for the techniques tested. LS-SVR generalizes better than ANNs, linear regression and a rain rate formula in rainfall estimation and for rainfall detection, SVR has a better performance than the other techniques.
引用
收藏
页码：229 / 251
页数：23
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