Learning networks in rainfall estimation

Theodore B. Trafalis*, Budi Santosa, Michael B. Richman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)229-251
Number of pages23
JournalComputational Management Science
Issue number3
Publication statusPublished - Jul 2005
Externally publishedYes


  • Artificial neural networks
  • Kernel functions
  • Radar
  • Rainfall estimation
  • Support vector machines


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