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>Radar rainfall rate fields for four selected quantitative precipitation estimation models (ZR2–L1, RF3, GBM3, and ELM5) for event #1 (August 28, 2018; 20 : 10). (a) ZR. (b) RF. (c) GB. (d) EL.</div>

Advances in Meteorology

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Figure 7

Figure 7: 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 7 | 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 