Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018

<div>(a) Root-mean-square error (RMSE), (b) correlation (COR), (c) mean absolute error (MAE), (d) mean bias (Mbias), and (e) relative RMSE (RRMSE) of rainfall rate estimations by the tested quantitative precipitation estimation models for all rainfall events studied.</div>

Advances in Meteorology

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Figure 3: Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018 

Figure 3 | Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018 