| Article ID: | iaor20071348 |
| Country: | Germany |
| Volume: | 2 |
| Issue: | 3 |
| Start Page Number: | 229 |
| End Page Number: | 251 |
| Publication Date: | Jul 2005 |
| Journal: | Computational Management Science |
| Authors: | Trafalis Theodore B., Santosa Budi, Richman Michael B. |
| Keywords: | neural networks |
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.