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>Locations of the Korea Meteorological Administration ground gauge stations (red dots) and Gwangdeoksan (GDK) radar (blue triangle) with its radar umbrella (effective radius: 100 km; maximum observation radius: 240 km). The figure in the right-hand panel presents the locations of the ground gauge stations (red dots) used in the current study.</div>

Advances in Meteorology

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Figure 1: 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 1 | 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 