Learning networks in rainfall estimation

Theodore Trafalis (), Budi Santosa () and Michael Richman ()

Computational Management Science, 2005, vol. 2, issue 3, 229-251

Abstract: 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. Copyright Springer-Verlag Berlin/Heidelberg 2005

Keywords: Artificial neural networks; support vector machines; kernel functions; rainfall estimation; radar (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1007/s10287-005-0026-0

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