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 of four selected quantitative precipitation estimation models (ZR2–L1, RF5, GBM2, and ELM5) for event #4 (November 8, 2018; 12 : 40). (a) ZR2-L1. (b) RF5. (c) GBM2. (d) ELM5.</div>

Advances in Meteorology

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Figure 8: 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 8 | 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 