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Learning networks in rainfall estimation

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  • Theodore Trafalis
  • Budi Santosa
  • Michael Richman

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

Suggested Citation

  • Theodore Trafalis & Budi Santosa & Michael Richman, 2005. "Learning networks in rainfall estimation," Computational Management Science, Springer, vol. 2(3), pages 229-251, July.
    • Handle: RePEc:spr:comgts:v:2:y:2005:i:3:p:229-251
    DOI: 10.1007/s10287-005-0026-0
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