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.
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Mathematics Subject Classification:
30C40
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Trafalis, T.B., Santosa, B. & Richman, M.B. Learning networks in rainfall estimation. CMS 2, 229–251 (2005). https://doi.org/10.1007/s10287-005-0026-0
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DOI: https://doi.org/10.1007/s10287-005-0026-0